A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle c...A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance(MVCA)algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently,without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore,MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay(< 100100 ms) and low packet loss(< 5%) can bring little influence to those trajectory planning algorithms that only depend on V2 V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.展开更多
Using stochastic dynamic simulation for railway vehicle collision still faces many challenges,such as high modelling complexity and time-consuming.To address the challenges,we introduce a novel data-driven stochastic ...Using stochastic dynamic simulation for railway vehicle collision still faces many challenges,such as high modelling complexity and time-consuming.To address the challenges,we introduce a novel data-driven stochastic process modelling(DSPM)approach into dynamic simulation of the railway vehicle collision.This DSPM approach consists of two steps:(i)process description,four kinds of kernels are used to describe the uncertainty inherent in collision processes;(ii)solving,stochastic variational inferences and mini-batch algorithms can then be used to accelerate computations of stochastic processes.By applying DSPM,Gaussian process regression(GPR)and finite element(FE)methods to two collision scenarios(i.e.lead car colliding with a rigid wall,and the lead car colliding with another lead car),we are able to achieve a comprehensive analysis.The comparison between the DSPM approach and the FE method revealed that the DSPM approach is capable of calculating the corresponding confidence interval,simultaneously improving the overall computational efficiency.Comparing the DSPM approach with the GPR method indicates that the DSPM approach has the ability to accurately describe the dynamic response under unknown conditions.Overall,this research demonstrates the feasibility and usability of the proposed DSPM approach for stochastic dynamics simulation of the railway vehicle collision.展开更多
Errant vehicles occur as a result of the driver losing control of the vehicle. This may be due to sudden illness, dozing off or skidding while attempting a manoeuvre. In containing such an errant vehicle on a highway,...Errant vehicles occur as a result of the driver losing control of the vehicle. This may be due to sudden illness, dozing off or skidding while attempting a manoeuvre. In containing such an errant vehicle on a highway, the priority is to avoid collision with other vehicles. A sloped highway median provides a run-off area for such vehicles where the vehicle can be slowed down and stopped without the danger of being re-directed into the path of other vehicles as may occur with edge barriers. Here, the effect of a containment barrier at the bottom of the sloped median is studied with a view to prevent the vehicle from being redirected outside the median after colliding with the barrier. The focus of this work is on the change of kinematic states due to the collision, so a momentum-based vehicle collision analysis is developed, with the collision energy loss related to the vehicle stiffness being considered by coefficient of restitution. The average maximum lateral displacements post-collision are read from the diagram of vehicle x-y trajectories. In this way, the most suitable median slope 1:6 is selected.展开更多
基金supported by the National Natural Science Foundation of China(61572229,6171101066)the Key Scientific and Technological Projects for Jilin Province Development Plan(20170204074GX,20180201068GX)Jilin Provincial International Cooperation Foundation(20180414015GH)。
文摘A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance(MVCA)algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently,without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore,MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay(< 100100 ms) and low packet loss(< 5%) can bring little influence to those trajectory planning algorithms that only depend on V2 V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.
基金supported by the National Key Research and Development Project(No.2019YFB1405401)the National Natural Science Foundation of China(No.5217120056)。
文摘Using stochastic dynamic simulation for railway vehicle collision still faces many challenges,such as high modelling complexity and time-consuming.To address the challenges,we introduce a novel data-driven stochastic process modelling(DSPM)approach into dynamic simulation of the railway vehicle collision.This DSPM approach consists of two steps:(i)process description,four kinds of kernels are used to describe the uncertainty inherent in collision processes;(ii)solving,stochastic variational inferences and mini-batch algorithms can then be used to accelerate computations of stochastic processes.By applying DSPM,Gaussian process regression(GPR)and finite element(FE)methods to two collision scenarios(i.e.lead car colliding with a rigid wall,and the lead car colliding with another lead car),we are able to achieve a comprehensive analysis.The comparison between the DSPM approach and the FE method revealed that the DSPM approach is capable of calculating the corresponding confidence interval,simultaneously improving the overall computational efficiency.Comparing the DSPM approach with the GPR method indicates that the DSPM approach has the ability to accurately describe the dynamic response under unknown conditions.Overall,this research demonstrates the feasibility and usability of the proposed DSPM approach for stochastic dynamics simulation of the railway vehicle collision.
文摘Errant vehicles occur as a result of the driver losing control of the vehicle. This may be due to sudden illness, dozing off or skidding while attempting a manoeuvre. In containing such an errant vehicle on a highway, the priority is to avoid collision with other vehicles. A sloped highway median provides a run-off area for such vehicles where the vehicle can be slowed down and stopped without the danger of being re-directed into the path of other vehicles as may occur with edge barriers. Here, the effect of a containment barrier at the bottom of the sloped median is studied with a view to prevent the vehicle from being redirected outside the median after colliding with the barrier. The focus of this work is on the change of kinematic states due to the collision, so a momentum-based vehicle collision analysis is developed, with the collision energy loss related to the vehicle stiffness being considered by coefficient of restitution. The average maximum lateral displacements post-collision are read from the diagram of vehicle x-y trajectories. In this way, the most suitable median slope 1:6 is selected.
