Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex ro...Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex road traffic environment of smart vehicles and other vehicles frequently experiences conflicting start and stop motion.The fine-grained scheduling of autonomous vehicles(AVs)at non-signalized intersections,which is a promising technique for exploring optimal driving paths for both assisted driving nowadays and driverless cars in the near future,has attracted significant attention owing to its high potential for improving road safety and traffic efficiency.Fine-grained scheduling primarily focuses on signalized intersection scenarios,as applying it directly to non-signalized intersections is challenging because each AV can move freely without traffic signal control.This may cause frequent driving collisions and low road traffic efficiency.Therefore,this study proposes a novel algorithm to address this issue.Our work focuses on the fine-grained scheduling of automated vehicles at non-signal intersections via dual reinforced training(FS-DRL).For FS-DRL,we first use a grid to describe the non-signalized intersection and propose a convolutional neural network(CNN)-based fast decision model that can rapidly yield a coarse-grained scheduling decision for each AV in a distributed manner.We then load these coarse-grained scheduling decisions onto a deep Q-learning network(DQN)for further evaluation.We use an adaptive learning rate to maximize the reward function and employ parameterεto tradeoff the fast speed of coarse-grained scheduling in the CNN and optimal fine-grained scheduling in the DQN.In addition,we prove that using this adaptive learning rate leads to a converged loss rate with an extremely small number of training loops.The simulation results show that compared with Dijkstra,RNN,and ant colony-based scheduling,FS-DRL yields a high accuracy of 96.5%on the sample,with improved performance of approximately 61.54%-85.37%in terms of the average conflict and traffic efficiency.展开更多
Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-...Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-ducted on trajectory tracking control and other aspects of CAVF,the quality of the extant literature varies consider-ably,and research content remains scattered.To better pro-mote the sustainable and healthy development of the CAVF field,this paper employs the mapping knowledge domain(MKD)methodology to comprehensively review and visual-ize the current research status in this domain.Based on this review,research themes,hotspots,research challenges,and future development directions are proposed.The findings suggest that the research on CAVF can be categorized into three primary developmental stages.China and the United States are the primary countries conducting CAVF research.There is a positive correlation between economic develop-ment and the generation of scientific research outcomes.Re-search institutions are predominantly concentrated in univer-sities.The field exhibits significant interdisciplinary and inte-gration characteristics,forming key research personnel and teams.It is expected that future research will concentrate on topics such as deep learning,trajectory optimization,energy management strategy,mixed vehicle platoon,and other re-lated subjects.Research on cognition-driven intelligent for-mation decision-making mechanisms,resilience-oriented for-mation safety assurance systems,multiobjective collabora-tive formation optimization strategies,and digital twin-driven formation system validation platforms represents key future development directions.展开更多
Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, curr...Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, current interdependence, and future potential through the lens of environmental, social, and economic sustainability. Historically, parking systems evolved from manual designs to automated processes yet remained focused on convenience rather than sustainability. Presently, advancements in smart infrastructure and vehicle-to-infrastructure (V2I) communication have enabled AVs and APS to operate as a cohesive system, optimizing space, energy, and transportation efficiency. Looking ahead, the seamless integration of AVs and APS into broader smart city ecosystems promises to redefine urban landscapes by repurposing traditional parking infrastructure into multifunctional spaces and supporting renewable energy initiatives. These technologies align with global sustainability goals by mitigating emissions, reducing urban sprawl, and fostering adaptive land uses. This reflection highlights the need for collaborative efforts among stakeholders to address regulatory and technological challenges, ensuring the equitable and efficient deployment of AVs and APS for smarter, greener cities.展开更多
The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system(STS).This system lets customers,video creators,and service providers directly connect with e...The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system(STS).This system lets customers,video creators,and service providers directly connect with each other.Blockchain-based STS devices need a lot of computer power to change different video feed quality and forms into different versions and structures that meet the needs of different users.On the other hand,existing blockchains can’t support live streaming because they take too long to process and don’t have enough computer power.Large amounts of video data being sent and analyzed put too much stress on networks for vehicles.A video surveillance method is suggested in this paper to improve the performance of the blockchain system’s data and lower the latency across the multiple access edge computing(MEC)system.The integration of MEC and blockchain for video surveillance in autonomous vehicles(IMEC-BVS)framework has been proposed.To deal with this problem,the joint optimization problem is shown using the actor-critical asynchronous advantage(ACAA)method and deep reinforcement training as a Markov Choice Progression(MCP).Simulation results show that the suggested method quickly converges and improves the performance of MEC and blockchain when used together for video surveillance in self-driving cars compared to other methods.展开更多
Commonly,the standards for the geometric design of roads refer to a given set of values for the friction coefficient(longitudinal and transverse friction).These"reference"values imply corresponding visibilit...Commonly,the standards for the geometric design of roads refer to a given set of values for the friction coefficient(longitudinal and transverse friction).These"reference"values imply corresponding visibility sights,curvature radii,and speed limits.Unfortunately,not only do these reference values not correspond to a given standard to measure them,but nothing is said about the decrease of the posted speed limit(variable speed limits)when roads become slippery and lanes for autonomous vehicle(AV)are concerned.Furthermore,the same assessment of the friction coefficient has plenty of uncertainties due to measurement device,temperature,location,time passed from the construction,alignment-related variables(e.g.,curve,tangent,transition curve,convexity/crests or concavity/sags,longitudinal slope,superelevation,and ruling gradient),and supplementary singularities such as joints and bridge approaches.All the issues above may harm road safety and the complexity of forensic investigations of pavements.Consequently,this study's objectives were confined to(1)carrying out friction measurements and analyzing the problem of friction decay over time;(2)setting up a method to lower the speed limits where friction decays are detected;(3)setting up a method to handle friction decays for autonomous vehicles.Results demonstrate that:(1)a power law describes how the speed limits are affected by friction;(2)for speeds up to 170 km/h,due to the lower reaction time,AV reaction distance is lower,which benefits AV traffic(lower stopping distance);(3)on the contrary,for higher values of friction and higher speeds,under the hypothesis of having the same reaction time law for non-AV(NAV)(i.e.,decreasing with the initial speed),AV speed limits become lower than NAV speed limits;(4)not only do comfort-based speed profiles for AVs bring higher braking distances,but also,in the median part(of the deceleration process),this could pose safety issues and reduce the distance between the available and the needed friction.展开更多
Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking proce...Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.展开更多
Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the...Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective are retrieved from the collection and inserted into the background, which is then sent to the modified 2D synthesis model to generate images. This pipeline introduces a perspective computing method, utilizes object retrieval to control the perspective accurately, and modifies a diffusion model to achieve high fidelity. Experiments show that our proposal gets a 2.36 Fréchet Inception Distance, which is lower than the competitive methods, indicating a superior realistic expression ability. When these images are used for augmentation in the semantic segmentation task, the performance of ResNet-50 on the target class can be improved by 4.47%. These results demonstrate that the proposed method can be used to generate cyclists in corner cases to augment model training data, further enhancing the perception capability of autonomous vehicles and improving the safety performance of autonomous driving technology.展开更多
Environmental perception is one of the key technologies to realize autonomous vehicles.Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system.Those sensors ...Environmental perception is one of the key technologies to realize autonomous vehicles.Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system.Those sensors are very sensitive to light or background conditions,which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running.In this paper,a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed.By introducing prior features to realize the lightweight network,the features of the input data can be extracted in real time.A new sensor reliability evaluation method is proposed by calculating the global and local confidence of sensors.Through the temporal and spatial correlation between sensor data,the sensor redundancy is utilized to diagnose the local and global confidence level of sensor data in real time,eliminate the fault data,and ensure the accuracy and reliability of data fusion.Experiments show that the network achieves state-of-the-art results in speed and accuracy,and can accurately detect the location of the target when some sensors are out of focus or out of order.The fusion framework proposed in this paper is proved to be effective for intelligent vehicles in terms of real-time performance and reliability.展开更多
Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless commu...Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless communication advances,vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention.In this paper,the recent studies on the planning and decision-making technologies at intersections are primarily overviewed.The general planning and decision-making approaches are presented,which include graph-based approach,prediction base approach,optimization-based approach and machine learning based approach.Since connected autonomous vehicles(CAVs)is the future direction for the automated driving area,we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies.Both four-way signalized and unsignalized intersection(s)are investigated under purely automated driving traffic and mixed traffic.The study benefit from current strategies,protocols,and simulation tools to help researchers identify the presented approaches’challenges and determine the research gaps,and several remaining possible research problems that need to be solved in the future.展开更多
The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced ...The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.展开更多
It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions.In this study,an adaptive path tracking control...It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions.In this study,an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm.The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model.To adaptively adjust the priorities of path tracking accuracy and vehicle stability,an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function.An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions.To ensure vehicle stability,the sideslip angle,yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame.It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and largecurvature conditions.展开更多
In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving...In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and roads.In this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology.This framework integrates vision feature based on PVRCNN.The normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection.The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.展开更多
Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly acc...Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.展开更多
As the complexity of autonomous vehicles(AVs)continues to increase and artificial intelligence algorithms are becoming increasingly ubiquitous,a novel safety concern known as the safety of the intended functionality(S...As the complexity of autonomous vehicles(AVs)continues to increase and artificial intelligence algorithms are becoming increasingly ubiquitous,a novel safety concern known as the safety of the intended functionality(SOTIF)has emerged,presenting significant challenges to the widespread deployment of AVs.SOTIF focuses on issues arising from the functional insufficiencies of the AVs’intended functionality or its implementation,apart from conventional safety considerations.From the systems engineering standpoint,this study offers a comprehensive exploration of the SOTIF landscape by reviewing academic research,practical activities,challenges,and perspectives across the development,verification,validation,and operation phases.Academic research encompasses system-level SOTIF studies and algorithm-related SOTIF issues and solutions.Moreover,it encapsulates practical SOTIF activities undertaken by corporations,government entities,and academic institutions spanning international and Chinese contexts,focusing on the overarching methodologies and practices in different phases.Finally,the paper presents future challenges and outlook pertaining to the development,verification,validation,and operation phases,motivating stakeholders to address the remaining obstacles and challenges.展开更多
Autonomous vehicles require safe motion planning in uncertain environments,which are largely caused by surrounding vehicles.In this paper,a driving environment uncertainty-aware motion planning framework is proposed t...Autonomous vehicles require safe motion planning in uncertain environments,which are largely caused by surrounding vehicles.In this paper,a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover.First,a 4-degree of freedom vehicle dynamics model,and a rollover risk index are introduced.Besides,the uncertainty of surrounding vehicles’position is processed and propagated based on the Extended Kalman Filter method.Then,the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles.In addition,the model predictive controller is designed as the motion planning framework which accounts for the rollover risk,the position uncertainty of the surrounding vehicles,and vehicle dynamic constraints of autonomous vehicles.Furthermore,two edge cases,the cut-in scenario,and merging scenario are designed.Finally,the safety,effectiveness,and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.展开更多
Many vehicle platoons are interrupted while traveling on roads,especially at urban signalized intersections.One reason for such interruptions is the inability to exchange real-time information between traditional huma...Many vehicle platoons are interrupted while traveling on roads,especially at urban signalized intersections.One reason for such interruptions is the inability to exchange real-time information between traditional human-driven vehicles and intersection infrastructure.Thus,this paper develops a Markov chain-based model to recognize platoons.A simulation experiment is performed in Vissim based on field data extracted from video recordings to prove the model’s applicability.The videos,recorded with a high-definition camera,contain field driving data from three Tesla vehicles,which can achieve Level 2 autonomous driving.The simulation results show that the recognition rate exceeds 80%when the connected and autonomous vehicle penetration rate is higher than 0.7.Whether a vehicle is upstream or downstream of an intersection also affects the performance of platoon recognition.The platoon recognition model developed in this paper can be used as a signal control input at intersections to reduce the unnecessary interruption of vehicle platoons and improve traffic efficiency.展开更多
The driver-automation shared driving is a transition to fully-autonomous driving,in which human driver and vehicular controller cooperatively share the control authority.This paper investigates the shared steering con...The driver-automation shared driving is a transition to fully-autonomous driving,in which human driver and vehicular controller cooperatively share the control authority.This paper investigates the shared steering control of semi-autonomous vehicles with uncertainty from imprecise parameter.By considering driver’s lane-keeping behavior on the vehicle system,a driver-automation shared driving model is introduced for control purpose.Based on the interval type-2(IT2)fuzzy theory,moreover,the driver-automation shared driving model with uncertainty from imprecise parameter is described using an IT2 fuzzy model.After that,the corresponding IT2 fuzzy controller is designed and a direct Lyapunov method is applied to analyze the system stability.In this work,sufficient design conditions in terms of linear matrix inequalities are derived,to guarantee the closed-loop stability of the driver-automation shared control system.In addition,an H∞performance is studied to ensure the robustness of control system.Finally,simulation-based results are provided to demonstrate the performance of proposed control method.Furthermore,an existing type-1 fuzzy controller is introduced as comparison to verify the superiority of the proposed IT2 fuzzy controller.展开更多
With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like autonomous driving is expected due to the impa...With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like autonomous driving is expected due to the impact of the differences between autonomous vehicles and human drivers on safety.Although human-like decision-making has become a research hotspot, a unified theory has not yet been formed, and there are significant differences in the implementation and performance of existing methods. This paper provides a comprehensive overview of human-like decision-making for autonomous vehicles. The following issues are discussed: 1) The intelligence level of most autonomous driving decision-making algorithms;2) The driving datasets and simulation platforms for testing and verifying human-like decision-making;3) The evaluation metrics of human-likeness;personalized driving;the application of decisionmaking in real traffic scenarios;and 4) The potential research direction of human-like driving. These research results are significant for creating interpretable human-like driving models and applying them in dynamic traffic scenarios. In the future, the combination of intuitive logical reasoning and hierarchical structure will be an important topic for further research. It is expected to meet the needs of human-like driving.展开更多
Model predictive control is widely used in the design of autonomous driving algorithms.However,its parameters are sensitive to dynamically varying driving conditions,making it difficult to be implemented into practice...Model predictive control is widely used in the design of autonomous driving algorithms.However,its parameters are sensitive to dynamically varying driving conditions,making it difficult to be implemented into practice.As a result,this study presents a self-learning algorithm based on reinforcement learning to tune a model predictive controller.Specifically,the proposed algorithm is used to extract features of dynamic traffic scenes and adjust the weight coefficients of the model predictive controller.In this method,a risk threshold model is proposed to classify the risk level of the scenes based on the scene features,and aid in the design of the reinforcement learning reward function and ultimately improve the adaptability of the model predictive controller to real-world scenarios.The proposed algorithm is compared to a pure model predictive controller in car-following case.According to the results,the proposed method enables autonomous vehicles to adjust the priority of performance indices reasonably in different scenarios according to risk variations,showing a good scenario adaptability with safety guaranteed.展开更多
Autonomous vehicles are essential for mobility in big cities,just like how elevators make high-rise buildings livable.While significant progress has been achieved over the last 15 years,there are still several remaini...Autonomous vehicles are essential for mobility in big cities,just like how elevators make high-rise buildings livable.While significant progress has been achieved over the last 15 years,there are still several remaining challenges,namely:cost,robust performance,and trust.To address these challenges,this paper discusses research at Mcity.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.61803206)Jiangsu Provincial Natural Science Foundation(Grant No.222300420468)Jiangsu Provincial key R&D Program(Grant No.BE2017008-2).
文摘Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex road traffic environment of smart vehicles and other vehicles frequently experiences conflicting start and stop motion.The fine-grained scheduling of autonomous vehicles(AVs)at non-signalized intersections,which is a promising technique for exploring optimal driving paths for both assisted driving nowadays and driverless cars in the near future,has attracted significant attention owing to its high potential for improving road safety and traffic efficiency.Fine-grained scheduling primarily focuses on signalized intersection scenarios,as applying it directly to non-signalized intersections is challenging because each AV can move freely without traffic signal control.This may cause frequent driving collisions and low road traffic efficiency.Therefore,this study proposes a novel algorithm to address this issue.Our work focuses on the fine-grained scheduling of automated vehicles at non-signal intersections via dual reinforced training(FS-DRL).For FS-DRL,we first use a grid to describe the non-signalized intersection and propose a convolutional neural network(CNN)-based fast decision model that can rapidly yield a coarse-grained scheduling decision for each AV in a distributed manner.We then load these coarse-grained scheduling decisions onto a deep Q-learning network(DQN)for further evaluation.We use an adaptive learning rate to maximize the reward function and employ parameterεto tradeoff the fast speed of coarse-grained scheduling in the CNN and optimal fine-grained scheduling in the DQN.In addition,we prove that using this adaptive learning rate leads to a converged loss rate with an extremely small number of training loops.The simulation results show that compared with Dijkstra,RNN,and ant colony-based scheduling,FS-DRL yields a high accuracy of 96.5%on the sample,with improved performance of approximately 61.54%-85.37%in terms of the average conflict and traffic efficiency.
基金The National Natural Science Foundation of China (No. 52302373, 52472317)the Natural Science Foundation of Beijing (No. L231023)the Beijing Nova Program (No. 20230484443)。
文摘Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-ducted on trajectory tracking control and other aspects of CAVF,the quality of the extant literature varies consider-ably,and research content remains scattered.To better pro-mote the sustainable and healthy development of the CAVF field,this paper employs the mapping knowledge domain(MKD)methodology to comprehensively review and visual-ize the current research status in this domain.Based on this review,research themes,hotspots,research challenges,and future development directions are proposed.The findings suggest that the research on CAVF can be categorized into three primary developmental stages.China and the United States are the primary countries conducting CAVF research.There is a positive correlation between economic develop-ment and the generation of scientific research outcomes.Re-search institutions are predominantly concentrated in univer-sities.The field exhibits significant interdisciplinary and inte-gration characteristics,forming key research personnel and teams.It is expected that future research will concentrate on topics such as deep learning,trajectory optimization,energy management strategy,mixed vehicle platoon,and other re-lated subjects.Research on cognition-driven intelligent for-mation decision-making mechanisms,resilience-oriented for-mation safety assurance systems,multiobjective collabora-tive formation optimization strategies,and digital twin-driven formation system validation platforms represents key future development directions.
文摘Integrating autonomous vehicles (AVs) and autonomous parking spaces (APS) marks a transformative development in urban mobility and sustainability. This paper reflects on these technologies’ historical evolution, current interdependence, and future potential through the lens of environmental, social, and economic sustainability. Historically, parking systems evolved from manual designs to automated processes yet remained focused on convenience rather than sustainability. Presently, advancements in smart infrastructure and vehicle-to-infrastructure (V2I) communication have enabled AVs and APS to operate as a cohesive system, optimizing space, energy, and transportation efficiency. Looking ahead, the seamless integration of AVs and APS into broader smart city ecosystems promises to redefine urban landscapes by repurposing traditional parking infrastructure into multifunctional spaces and supporting renewable energy initiatives. These technologies align with global sustainability goals by mitigating emissions, reducing urban sprawl, and fostering adaptive land uses. This reflection highlights the need for collaborative efforts among stakeholders to address regulatory and technological challenges, ensuring the equitable and efficient deployment of AVs and APS for smarter, greener cities.
文摘The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system(STS).This system lets customers,video creators,and service providers directly connect with each other.Blockchain-based STS devices need a lot of computer power to change different video feed quality and forms into different versions and structures that meet the needs of different users.On the other hand,existing blockchains can’t support live streaming because they take too long to process and don’t have enough computer power.Large amounts of video data being sent and analyzed put too much stress on networks for vehicles.A video surveillance method is suggested in this paper to improve the performance of the blockchain system’s data and lower the latency across the multiple access edge computing(MEC)system.The integration of MEC and blockchain for video surveillance in autonomous vehicles(IMEC-BVS)framework has been proposed.To deal with this problem,the joint optimization problem is shown using the actor-critical asynchronous advantage(ACAA)method and deep reinforcement training as a Markov Choice Progression(MCP).Simulation results show that the suggested method quickly converges and improves the performance of MEC and blockchain when used together for video surveillance in self-driving cars compared to other methods.
文摘Commonly,the standards for the geometric design of roads refer to a given set of values for the friction coefficient(longitudinal and transverse friction).These"reference"values imply corresponding visibility sights,curvature radii,and speed limits.Unfortunately,not only do these reference values not correspond to a given standard to measure them,but nothing is said about the decrease of the posted speed limit(variable speed limits)when roads become slippery and lanes for autonomous vehicle(AV)are concerned.Furthermore,the same assessment of the friction coefficient has plenty of uncertainties due to measurement device,temperature,location,time passed from the construction,alignment-related variables(e.g.,curve,tangent,transition curve,convexity/crests or concavity/sags,longitudinal slope,superelevation,and ruling gradient),and supplementary singularities such as joints and bridge approaches.All the issues above may harm road safety and the complexity of forensic investigations of pavements.Consequently,this study's objectives were confined to(1)carrying out friction measurements and analyzing the problem of friction decay over time;(2)setting up a method to lower the speed limits where friction decays are detected;(3)setting up a method to handle friction decays for autonomous vehicles.Results demonstrate that:(1)a power law describes how the speed limits are affected by friction;(2)for speeds up to 170 km/h,due to the lower reaction time,AV reaction distance is lower,which benefits AV traffic(lower stopping distance);(3)on the contrary,for higher values of friction and higher speeds,under the hypothesis of having the same reaction time law for non-AV(NAV)(i.e.,decreasing with the initial speed),AV speed limits become lower than NAV speed limits;(4)not only do comfort-based speed profiles for AVs bring higher braking distances,but also,in the median part(of the deceleration process),this could pose safety issues and reduce the distance between the available and the needed friction.
基金Supported by National Natural Science Foundation of China(Grant No.52405104)Jiangxi Provincial Natural Science Foundation(Grant Nos.20242BAB20249 and 20232BAB204041)Science and Technology Project of Department of Transportation of Jiangxi Province(Grant No.2025QN003).
文摘Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.
基金supported by the Cultivation Program for Major Scientific Research Projects of Harbin Institute of Technology(ZDXMPY20180109).
文摘Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective are retrieved from the collection and inserted into the background, which is then sent to the modified 2D synthesis model to generate images. This pipeline introduces a perspective computing method, utilizes object retrieval to control the perspective accurately, and modifies a diffusion model to achieve high fidelity. Experiments show that our proposal gets a 2.36 Fréchet Inception Distance, which is lower than the competitive methods, indicating a superior realistic expression ability. When these images are used for augmentation in the semantic segmentation task, the performance of ResNet-50 on the target class can be improved by 4.47%. These results demonstrate that the proposed method can be used to generate cyclists in corner cases to augment model training data, further enhancing the perception capability of autonomous vehicles and improving the safety performance of autonomous driving technology.
基金Supported by the National Natural Science Foundation of China(Grant U1964201,Grant 61790562 and Grant 61803120)by the Fundamental Research Fundsfor the Central Universities.
文摘Environmental perception is one of the key technologies to realize autonomous vehicles.Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system.Those sensors are very sensitive to light or background conditions,which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running.In this paper,a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed.By introducing prior features to realize the lightweight network,the features of the input data can be extracted in real time.A new sensor reliability evaluation method is proposed by calculating the global and local confidence of sensors.Through the temporal and spatial correlation between sensor data,the sensor redundancy is utilized to diagnose the local and global confidence level of sensor data in real time,eliminate the fault data,and ensure the accuracy and reliability of data fusion.Experiments show that the network achieves state-of-the-art results in speed and accuracy,and can accurately detect the location of the target when some sensors are out of focus or out of order.The fusion framework proposed in this paper is proved to be effective for intelligent vehicles in terms of real-time performance and reliability.
文摘Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless communication advances,vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention.In this paper,the recent studies on the planning and decision-making technologies at intersections are primarily overviewed.The general planning and decision-making approaches are presented,which include graph-based approach,prediction base approach,optimization-based approach and machine learning based approach.Since connected autonomous vehicles(CAVs)is the future direction for the automated driving area,we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies.Both four-way signalized and unsignalized intersection(s)are investigated under purely automated driving traffic and mixed traffic.The study benefit from current strategies,protocols,and simulation tools to help researchers identify the presented approaches’challenges and determine the research gaps,and several remaining possible research problems that need to be solved in the future.
基金supported by the FundamentalResearch Funds for the Central Universities(2662019QD002)
文摘The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.
基金Supported by the Foundation of Key Laboratory of Vehicle Advanced ManufacturingMeasuring and Control Technology(Beijing Jiaotong University)+1 种基金Ministry of Education,China(Grant No.014062522006)National Key Research Development Program of China(Grant No.2017YFB0103701)。
文摘It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions.In this study,an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm.The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model.To adaptively adjust the priorities of path tracking accuracy and vehicle stability,an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function.An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions.To ensure vehicle stability,the sideslip angle,yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame.It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and largecurvature conditions.
基金This research was supported by the National Key Research and Development Program of China under Grant No.2017YFB0102502the Beijing Municipal Natural Science Foundation No.L191001+2 种基金the National Natural Science Foundation of China under Grant No.61672082 and 61822101the Newton Advanced Fellowship under Grant No.62061130221the Young Elite Scientists Sponsorship Program by Hunan Provincial Department of Education under Grant No.18B142.
文摘In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and roads.In this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology.This framework integrates vision feature based on PVRCNN.The normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection.The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.
文摘Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.
基金supported by the National Science Foundation of China Project(52072215,U1964203,52242213,and 52221005)National Key Research and Development(R&D)Program of China(2022YFB2503003)State Key Laboratory of Intelligent Green Vehicle and Mobility。
文摘As the complexity of autonomous vehicles(AVs)continues to increase and artificial intelligence algorithms are becoming increasingly ubiquitous,a novel safety concern known as the safety of the intended functionality(SOTIF)has emerged,presenting significant challenges to the widespread deployment of AVs.SOTIF focuses on issues arising from the functional insufficiencies of the AVs’intended functionality or its implementation,apart from conventional safety considerations.From the systems engineering standpoint,this study offers a comprehensive exploration of the SOTIF landscape by reviewing academic research,practical activities,challenges,and perspectives across the development,verification,validation,and operation phases.Academic research encompasses system-level SOTIF studies and algorithm-related SOTIF issues and solutions.Moreover,it encapsulates practical SOTIF activities undertaken by corporations,government entities,and academic institutions spanning international and Chinese contexts,focusing on the overarching methodologies and practices in different phases.Finally,the paper presents future challenges and outlook pertaining to the development,verification,validation,and operation phases,motivating stakeholders to address the remaining obstacles and challenges.
基金National Key R&D Program of China(Grant No.2020YFB1600303)National Natural Science Foundation of China(Grant Nos.U1964203,52072215)Chongqing Municipal Natural Science Foundation of China(Grant No.cstc2020jcyj-msxmX0956).
文摘Autonomous vehicles require safe motion planning in uncertain environments,which are largely caused by surrounding vehicles.In this paper,a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover.First,a 4-degree of freedom vehicle dynamics model,and a rollover risk index are introduced.Besides,the uncertainty of surrounding vehicles’position is processed and propagated based on the Extended Kalman Filter method.Then,the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles.In addition,the model predictive controller is designed as the motion planning framework which accounts for the rollover risk,the position uncertainty of the surrounding vehicles,and vehicle dynamic constraints of autonomous vehicles.Furthermore,two edge cases,the cut-in scenario,and merging scenario are designed.Finally,the safety,effectiveness,and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.
基金Project(71871013)supported by the National Natural Science Foundation of China。
文摘Many vehicle platoons are interrupted while traveling on roads,especially at urban signalized intersections.One reason for such interruptions is the inability to exchange real-time information between traditional human-driven vehicles and intersection infrastructure.Thus,this paper develops a Markov chain-based model to recognize platoons.A simulation experiment is performed in Vissim based on field data extracted from video recordings to prove the model’s applicability.The videos,recorded with a high-definition camera,contain field driving data from three Tesla vehicles,which can achieve Level 2 autonomous driving.The simulation results show that the recognition rate exceeds 80%when the connected and autonomous vehicle penetration rate is higher than 0.7.Whether a vehicle is upstream or downstream of an intersection also affects the performance of platoon recognition.The platoon recognition model developed in this paper can be used as a signal control input at intersections to reduce the unnecessary interruption of vehicle platoons and improve traffic efficiency.
基金Supported by Defense Industrial Technology Development Program.
文摘The driver-automation shared driving is a transition to fully-autonomous driving,in which human driver and vehicular controller cooperatively share the control authority.This paper investigates the shared steering control of semi-autonomous vehicles with uncertainty from imprecise parameter.By considering driver’s lane-keeping behavior on the vehicle system,a driver-automation shared driving model is introduced for control purpose.Based on the interval type-2(IT2)fuzzy theory,moreover,the driver-automation shared driving model with uncertainty from imprecise parameter is described using an IT2 fuzzy model.After that,the corresponding IT2 fuzzy controller is designed and a direct Lyapunov method is applied to analyze the system stability.In this work,sufficient design conditions in terms of linear matrix inequalities are derived,to guarantee the closed-loop stability of the driver-automation shared control system.In addition,an H∞performance is studied to ensure the robustness of control system.Finally,simulation-based results are provided to demonstrate the performance of proposed control method.Furthermore,an existing type-1 fuzzy controller is introduced as comparison to verify the superiority of the proposed IT2 fuzzy controller.
基金supported by the National Key R&D Program of China (2022YFB2502900)the National Natural Science Foundation of China (62088102, 61790563)。
文摘With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like autonomous driving is expected due to the impact of the differences between autonomous vehicles and human drivers on safety.Although human-like decision-making has become a research hotspot, a unified theory has not yet been formed, and there are significant differences in the implementation and performance of existing methods. This paper provides a comprehensive overview of human-like decision-making for autonomous vehicles. The following issues are discussed: 1) The intelligence level of most autonomous driving decision-making algorithms;2) The driving datasets and simulation platforms for testing and verifying human-like decision-making;3) The evaluation metrics of human-likeness;personalized driving;the application of decisionmaking in real traffic scenarios;and 4) The potential research direction of human-like driving. These research results are significant for creating interpretable human-like driving models and applying them in dynamic traffic scenarios. In the future, the combination of intuitive logical reasoning and hierarchical structure will be an important topic for further research. It is expected to meet the needs of human-like driving.
基金Supported by National Key R&D Program of China(Grant No.2022YFB2502900)Fundamental Research Funds for the Central Universities of China,Science and Technology Commission of Shanghai Municipality of China(Grant No.21ZR1465900)Shanghai Gaofeng&Gaoyuan Project for University Academic Program Development of China.
文摘Model predictive control is widely used in the design of autonomous driving algorithms.However,its parameters are sensitive to dynamically varying driving conditions,making it difficult to be implemented into practice.As a result,this study presents a self-learning algorithm based on reinforcement learning to tune a model predictive controller.Specifically,the proposed algorithm is used to extract features of dynamic traffic scenes and adjust the weight coefficients of the model predictive controller.In this method,a risk threshold model is proposed to classify the risk level of the scenes based on the scene features,and aid in the design of the reinforcement learning reward function and ultimately improve the adaptability of the model predictive controller to real-world scenarios.The proposed algorithm is compared to a pure model predictive controller in car-following case.According to the results,the proposed method enables autonomous vehicles to adjust the priority of performance indices reasonably in different scenarios according to risk variations,showing a good scenario adaptability with safety guaranteed.
文摘Autonomous vehicles are essential for mobility in big cities,just like how elevators make high-rise buildings livable.While significant progress has been achieved over the last 15 years,there are still several remaining challenges,namely:cost,robust performance,and trust.To address these challenges,this paper discusses research at Mcity.
