The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety r...The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also quickly detect changes in the surrounding environment. In autonomous vehicle research, the environment perception system is one of the key components of development. Environment perception systems allow the vehicle to understand its surroundings. This is done by using cameras, light detection and ranging (LiDAR), with other sensor systems and modalities. Deep learning computer vision algorithms have been shown to be the strongest tool for translating camera data into accurate and safe traversability decisions regarding the environment surrounding a vehicle. In order for a vehicle to safely traverse an area in real time, these computer vision algorithms must be accurate and have low latency. While much research has studied autonomous driving for traversing well-structured urban environments, limited research exists evaluating perception system improvements in off-road settings. This research aims to investigate the adaptability of several existing deep-learning architectures for semantic segmentation in off-road environments. Previous studies of two Convolutional Neural Network (CNN) architectures are included for comparison with new evaluation of Vision Transformer (ViT) architectures for semantic segmentation. Our results demonstrate viability of ViT architectures for off-road perception systems, having a strong segmentation accuracy, lower inference speed and memory footprint compared to previous results with CNN architectures.展开更多
Short suspension system has an indispensable effect on vehicle handling and ride,so,optimization of vehicle suspension system is one of the most effective methods,which could considerably enhance the vehicle stability...Short suspension system has an indispensable effect on vehicle handling and ride,so,optimization of vehicle suspension system is one of the most effective methods,which could considerably enhance the vehicle stability and controllability.Motion control,stability maintenance and ride comfort improvement are fundamental issues in design of suspension system of off-road vehicles.In this work,a dependent suspension system mostly used in off-road vehicles is modeled using Trucksim software.Then,geometric parameters of suspension system are optimized using integrated anti-roll bar and coiling spring in a way that ride comfort,handling and stability of vehicle are improved.The simulation results of suspension system and variations of geometric parameters due to road roughness and different steering angles are presented in Trucksim and effects of optimization of suspension system during various driving maneuvers in both optimized and un-optimized conditions are compared.The simulation results indicate that the type of suspension system and geometric parameters have significant effect on vehicle performance.展开更多
Increasing frame torsional stiffness of off-road vehicle will lead to the decrease of body torsional deformation, but the increase of torsional loads of frame and suspension system and the decrease of wheel adhesive w...Increasing frame torsional stiffness of off-road vehicle will lead to the decrease of body torsional deformation, but the increase of torsional loads of frame and suspension system and the decrease of wheel adhesive weight. In severe case, a certain wheel will be out of contact with road surface. Appropriate matching of body, frame and suspension torsional stiffnesses is a difficult problem for off-road vehicle design. In this paper, these theoretically analytic models of the entire vehicle, body, frame and suspension torsional stiffness are constructed based on the geometry and mechanism of a light off-road vehicle＇s body, frame and suspension. The body and frame torsional stiffnesses can be calculated by applying body CAE method, meanwhile the suspension＇s rolling angle stiffness can be obtained by the bench test of the suspension＇s elastic elements. Through fixing the entire vehicle, using sole timber to raise wheels to simulate the road impact on a certain wheel, the entire vehicle torsional stiffness can be calculated on the geometric relation and loads of testing. Finally some appropriate matching principles of the body, frame and suspension torsional stiffness are summarized according to the test and analysis results. The conclusion can reveal the significance of the suspension torsional stiffness on off-road vehicle＇s torsion-absorbing capability. The results could serve as a reference for the design of other off-road vehicles.展开更多
In order to evaluate the impact of off-road terrains on the ride comfort of construction vehicles,a nonlinear dynamic model of the construction vehicles interacting with the terrain deformations is established based o...In order to evaluate the impact of off-road terrains on the ride comfort of construction vehicles,a nonlinear dynamic model of the construction vehicles interacting with the terrain deformations is established based on Matlab/Simulink software.The weighted root mean square(RMS)acceleration responses and the power spectral density(PSD)acceleration responses of the driver s seat heave,the pitch and roll angle of the cab in the low-frequency region are chosen as objective functions under different operation conditions of the vehicle.The results show that the impact of off-road terrains on the driver s ride comfort and health is clear under various conditions of deformable terrains and range of vehicle velocities.In particular,the driver s ride comfort is greatly affected by a soil terrain while the comfortable shake of the driver is strongly affected by a sand terrain.In addition,when the vehicle travels on a poor soil terrain in the frequency range below 4 Hz,more resonance peaks of acceleration PSD responses occurred than that on a rigid road of ISO 2631-1 level C.Thus,the driver s health is significantly affected by the deformable terrain in a low-frequency range.展开更多
Run-off-road crashes in the United States have become a major cause of serious injuries and fatalities. A significant portion of run-off-road crashes are single vehicle crashes that occur due to collisions with fixed ...Run-off-road crashes in the United States have become a major cause of serious injuries and fatalities. A significant portion of run-off-road crashes are single vehicle crashes that occur due to collisions with fixed objects and overturning. These crashes typically tend to be more severe than other types of crashes. Single vehicle run-off-road crashes that occurred between 2004 and 2008 were extracted from Kansas Accident Reporting System (KARS) database to identify the important factors that affected their severity. Different driver, vehicle, road, crash, and environment related factors that influence crash severity are identified by using binary logit models. Three models were developed to take different levels of crash severity as the response variables. The first model taking fatal or incapacitating crashes as the response variable seems to better fit the data than the other two developed models. The variables that were found to increase the probability of run-off-road crash severity are driver related factors such as driver ejection, being an older driver, alcohol involvement, license state, driver being at fault, medical condition of the driver;road related factors such as speed, asphalt road surface, dry road condition;time related factors such as crashes occurring between 6 pm and midnight;environment related factors such as daylight;vehicle related factors such as being an SUV, motorcycles, vehicle getting destroyed or disabled, vehicle maneuver being straight or passing;and fixed object types such as trees and ditches.展开更多
This paper presents a software framework for off-road autonomous robot navigation system.With the requirements of accurate terrain perception and instantaneous obstacles detection,one navigation software framework was...This paper presents a software framework for off-road autonomous robot navigation system.With the requirements of accurate terrain perception and instantaneous obstacles detection,one navigation software framework was advanced based on the principles of "three layer architecture" of intelligence system.Utilized the technologies of distributed system,machine learning and multiple sensor fusion,individual functional module was discussed.This paper aims to provide a framework reference for autonomous robot navigation system design.展开更多
The controllable suspension system can improve the performances of off-road vehicles both on road and cross-country. So far, four controllable suspensions, that is, body height control, active, semi-active and slow-ac...The controllable suspension system can improve the performances of off-road vehicles both on road and cross-country. So far, four controllable suspensions, that is, body height control, active, semi-active and slow-active suspensions, have been developed. For off-road vehicles, the slow-active suspension and the semi-active suspension which have controllable stiffness, damping and body height are more appropriate to use. For many years, some control methodologies for controllable suspension systems have been developed along with the development of modern control theory, and two or more original control methods are integrated as a new control method. Today, for military or civilian off-road vehicles, the R&D of controllable suspension systems is ongoing.展开更多
Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees l...Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.展开更多
To realize the widespread application and continuous functional development of intelligent vehicles,test and evaluation of vehicle's functionality and Safety Performance in complex off-road scenarios are fundament...To realize the widespread application and continuous functional development of intelligent vehicles,test and evaluation of vehicle's functionality and Safety Performance in complex off-road scenarios are fundamental.Since traditional distance-based road tests cannot meet the evolving test requirements,a method to design the function-based off-road testing scenario library for intelligent vehicles(IV)is proposed in this paper.The testing scenario library is defined as a critical set of scenarios that can be used for IV tests.First,for the complex and diverse off-road scenarios,a hierarchical,structural model of the test scenario is built.Then,the critical test scenarios are selected adaptively according to the vehicle model to be tested.Next,those parameters representing the challenging test scenarios are selected.The selected parameters need to fit the natural distribution probability of scenarios.The critical test-scenario library is built combing these parameters with the structural model.Finally,the test scenarios that are most approximate to the natural driving scenario are determined with importance sampling theory.The test-scenario library built with this method can provide more critical test scenarios,and is widely applicable despite different vehicle models.Verified by simulation in the off-road interaction scenarios,test would be accelerated significantly with this method,about 800 times faster than testing in the natural road environment.展开更多
With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a compl...With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity.A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi-sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments.State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.展开更多
文摘The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also quickly detect changes in the surrounding environment. In autonomous vehicle research, the environment perception system is one of the key components of development. Environment perception systems allow the vehicle to understand its surroundings. This is done by using cameras, light detection and ranging (LiDAR), with other sensor systems and modalities. Deep learning computer vision algorithms have been shown to be the strongest tool for translating camera data into accurate and safe traversability decisions regarding the environment surrounding a vehicle. In order for a vehicle to safely traverse an area in real time, these computer vision algorithms must be accurate and have low latency. While much research has studied autonomous driving for traversing well-structured urban environments, limited research exists evaluating perception system improvements in off-road settings. This research aims to investigate the adaptability of several existing deep-learning architectures for semantic segmentation in off-road environments. Previous studies of two Convolutional Neural Network (CNN) architectures are included for comparison with new evaluation of Vision Transformer (ViT) architectures for semantic segmentation. Our results demonstrate viability of ViT architectures for off-road perception systems, having a strong segmentation accuracy, lower inference speed and memory footprint compared to previous results with CNN architectures.
文摘Short suspension system has an indispensable effect on vehicle handling and ride,so,optimization of vehicle suspension system is one of the most effective methods,which could considerably enhance the vehicle stability and controllability.Motion control,stability maintenance and ride comfort improvement are fundamental issues in design of suspension system of off-road vehicles.In this work,a dependent suspension system mostly used in off-road vehicles is modeled using Trucksim software.Then,geometric parameters of suspension system are optimized using integrated anti-roll bar and coiling spring in a way that ride comfort,handling and stability of vehicle are improved.The simulation results of suspension system and variations of geometric parameters due to road roughness and different steering angles are presented in Trucksim and effects of optimization of suspension system during various driving maneuvers in both optimized and un-optimized conditions are compared.The simulation results indicate that the type of suspension system and geometric parameters have significant effect on vehicle performance.
文摘Increasing frame torsional stiffness of off-road vehicle will lead to the decrease of body torsional deformation, but the increase of torsional loads of frame and suspension system and the decrease of wheel adhesive weight. In severe case, a certain wheel will be out of contact with road surface. Appropriate matching of body, frame and suspension torsional stiffnesses is a difficult problem for off-road vehicle design. In this paper, these theoretically analytic models of the entire vehicle, body, frame and suspension torsional stiffness are constructed based on the geometry and mechanism of a light off-road vehicle＇s body, frame and suspension. The body and frame torsional stiffnesses can be calculated by applying body CAE method, meanwhile the suspension＇s rolling angle stiffness can be obtained by the bench test of the suspension＇s elastic elements. Through fixing the entire vehicle, using sole timber to raise wheels to simulate the road impact on a certain wheel, the entire vehicle torsional stiffness can be calculated on the geometric relation and loads of testing. Finally some appropriate matching principles of the body, frame and suspension torsional stiffness are summarized according to the test and analysis results. The conclusion can reveal the significance of the suspension torsional stiffness on off-road vehicle＇s torsion-absorbing capability. The results could serve as a reference for the design of other off-road vehicles.
基金The Science and Technology Support Program of Jiangsu Province(No.BE2014133)the Prospective Joint Research Program of Jiangsu Province(No.BY2014127-01)
文摘In order to evaluate the impact of off-road terrains on the ride comfort of construction vehicles,a nonlinear dynamic model of the construction vehicles interacting with the terrain deformations is established based on Matlab/Simulink software.The weighted root mean square(RMS)acceleration responses and the power spectral density(PSD)acceleration responses of the driver s seat heave,the pitch and roll angle of the cab in the low-frequency region are chosen as objective functions under different operation conditions of the vehicle.The results show that the impact of off-road terrains on the driver s ride comfort and health is clear under various conditions of deformable terrains and range of vehicle velocities.In particular,the driver s ride comfort is greatly affected by a soil terrain while the comfortable shake of the driver is strongly affected by a sand terrain.In addition,when the vehicle travels on a poor soil terrain in the frequency range below 4 Hz,more resonance peaks of acceleration PSD responses occurred than that on a rigid road of ISO 2631-1 level C.Thus,the driver s health is significantly affected by the deformable terrain in a low-frequency range.
文摘Run-off-road crashes in the United States have become a major cause of serious injuries and fatalities. A significant portion of run-off-road crashes are single vehicle crashes that occur due to collisions with fixed objects and overturning. These crashes typically tend to be more severe than other types of crashes. Single vehicle run-off-road crashes that occurred between 2004 and 2008 were extracted from Kansas Accident Reporting System (KARS) database to identify the important factors that affected their severity. Different driver, vehicle, road, crash, and environment related factors that influence crash severity are identified by using binary logit models. Three models were developed to take different levels of crash severity as the response variables. The first model taking fatal or incapacitating crashes as the response variable seems to better fit the data than the other two developed models. The variables that were found to increase the probability of run-off-road crash severity are driver related factors such as driver ejection, being an older driver, alcohol involvement, license state, driver being at fault, medical condition of the driver;road related factors such as speed, asphalt road surface, dry road condition;time related factors such as crashes occurring between 6 pm and midnight;environment related factors such as daylight;vehicle related factors such as being an SUV, motorcycles, vehicle getting destroyed or disabled, vehicle maneuver being straight or passing;and fixed object types such as trees and ditches.
基金supported by Nature Science Foundation of Zhejiang Province(No. Y10808 83 and No.Y1080967)Supported by Preferential Subject Key Project of Zhejiang Province(No.2008C13G2040006)
文摘This paper presents a software framework for off-road autonomous robot navigation system.With the requirements of accurate terrain perception and instantaneous obstacles detection,one navigation software framework was advanced based on the principles of "three layer architecture" of intelligence system.Utilized the technologies of distributed system,machine learning and multiple sensor fusion,individual functional module was discussed.This paper aims to provide a framework reference for autonomous robot navigation system design.
文摘The controllable suspension system can improve the performances of off-road vehicles both on road and cross-country. So far, four controllable suspensions, that is, body height control, active, semi-active and slow-active suspensions, have been developed. For off-road vehicles, the slow-active suspension and the semi-active suspension which have controllable stiffness, damping and body height are more appropriate to use. For many years, some control methodologies for controllable suspension systems have been developed along with the development of modern control theory, and two or more original control methods are integrated as a new control method. Today, for military or civilian off-road vehicles, the R&D of controllable suspension systems is ongoing.
基金supported in part by 14th Five Year National Key R&D Program Project(Project Number:2023YFB3211001)the National Natural Science Foundation of China(62273339,U24A201397).
文摘Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.
基金National Natural Science Foundation of China No.U19A2083.
文摘To realize the widespread application and continuous functional development of intelligent vehicles,test and evaluation of vehicle's functionality and Safety Performance in complex off-road scenarios are fundamental.Since traditional distance-based road tests cannot meet the evolving test requirements,a method to design the function-based off-road testing scenario library for intelligent vehicles(IV)is proposed in this paper.The testing scenario library is defined as a critical set of scenarios that can be used for IV tests.First,for the complex and diverse off-road scenarios,a hierarchical,structural model of the test scenario is built.Then,the critical test scenarios are selected adaptively according to the vehicle model to be tested.Next,those parameters representing the challenging test scenarios are selected.The selected parameters need to fit the natural distribution probability of scenarios.The critical test-scenario library is built combing these parameters with the structural model.Finally,the test scenarios that are most approximate to the natural driving scenario are determined with importance sampling theory.The test-scenario library built with this method can provide more critical test scenarios,and is widely applicable despite different vehicle models.Verified by simulation in the off-road interaction scenarios,test would be accelerated significantly with this method,about 800 times faster than testing in the natural road environment.
基金Project supported by the National Natural Science Foundation of China(Nos.61603303,61803309,and 61703343)the Natural Science Foundation of Shaanxi Province,China(No.2018JQ6070)+1 种基金the China Postdoctoral Science Foundation(No.2018M633574)the Fundamental Research Funds for the Central Universities,China(Nos.3102019ZDHKY02 and3102018JCC003)。
文摘With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity.A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi-sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments.State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.
