Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autore...Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.展开更多
The deployment of autonomous vehicles is rapidly evolving,bringing both unprecedented opportunities and challenges.To achieve safer autonomous driving,the role of testing has become paramount,serving as the cornerston...The deployment of autonomous vehicles is rapidly evolving,bringing both unprecedented opportunities and challenges.To achieve safer autonomous driving,the role of testing has become paramount,serving as the cornerstone for validating the performance,robustness,and trustworthiness of autonomous driving systems.Unlike traditional vehicle testing,the focus of autonomous vehicle testing is on their intended driving functionality in complex driving environments.The burgeoning field of autonomous vehicle testing requires not only an understanding of current technological landscapes but also a proactive exploration of innovative solutions to advance the field,particularly through the application of artificial intelligence and simulation-based approaches.Within the spectrum of autonomous vehicle testing,the evolving challenges demand advancements in key technologies,including the construction and generalization of testing scenarios,the recognition of risky scenarios,driving behavior modeling,comprehensive testing evaluation,evidence-based testing protocols,simulation toolchain development,and more.展开更多
This study focuses on predicting the motion states and intentions of HDVs at unsignalized intersections.On the basis of a risk field-driven driving behavior model for uncontrolled intersections,multiple motion hypothe...This study focuses on predicting the motion states and intentions of HDVs at unsignalized intersections.On the basis of a risk field-driven driving behavior model for uncontrolled intersections,multiple motion hypotheses are formulated to characterize the motion planning process of drivers in multivehicle conflict scenarios.Each motion hypothesis is modeled and expressed separately via the extended Kalman filter(EKF)model.These EKF models were combined to construct an interacting multiple model(IMM)framework.This framework estimates which motion hypothesis the driver is more likely to adopt as a strategy.By integrating the predictions of multiple motion hypotheses,more accurate predictions are obtained.Ultimately,it estimates the driver's travel path and acceptable risk level and predicts the spatiotemporal trajectory of HDVs within a future time window.展开更多
文摘Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.
文摘The deployment of autonomous vehicles is rapidly evolving,bringing both unprecedented opportunities and challenges.To achieve safer autonomous driving,the role of testing has become paramount,serving as the cornerstone for validating the performance,robustness,and trustworthiness of autonomous driving systems.Unlike traditional vehicle testing,the focus of autonomous vehicle testing is on their intended driving functionality in complex driving environments.The burgeoning field of autonomous vehicle testing requires not only an understanding of current technological landscapes but also a proactive exploration of innovative solutions to advance the field,particularly through the application of artificial intelligence and simulation-based approaches.Within the spectrum of autonomous vehicle testing,the evolving challenges demand advancements in key technologies,including the construction and generalization of testing scenarios,the recognition of risky scenarios,driving behavior modeling,comprehensive testing evaluation,evidence-based testing protocols,simulation toolchain development,and more.
基金supported by the National Natural Science Foundation of China(52131204 and 52102393)the Fundamental Research Funds for the Central Universities.
文摘This study focuses on predicting the motion states and intentions of HDVs at unsignalized intersections.On the basis of a risk field-driven driving behavior model for uncontrolled intersections,multiple motion hypotheses are formulated to characterize the motion planning process of drivers in multivehicle conflict scenarios.Each motion hypothesis is modeled and expressed separately via the extended Kalman filter(EKF)model.These EKF models were combined to construct an interacting multiple model(IMM)framework.This framework estimates which motion hypothesis the driver is more likely to adopt as a strategy.By integrating the predictions of multiple motion hypotheses,more accurate predictions are obtained.Ultimately,it estimates the driver's travel path and acceptable risk level and predicts the spatiotemporal trajectory of HDVs within a future time window.
