CPTPS: A Cyber-Physical Trajectory Planning System for Trajectory Planning of Autonomous Vehicles in Unstructured Space BYD Auto Industry

Format:

Book

Conference/Event

Author/Creator:

Ma, Shizi, author.

Contributor:

Lai, Daoyin

Ma, Zhitao

Qi, Zhiguo

Shi, Ying

Yang, Zhongkai

Conference Name:

SAE 2024 Intelligent Urban Air Mobility Symposium (2024-09-06 : Hangzhou, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Internet of vehicles (IoV) system as a typical application scenario of smart city, trajectory planning is one of the key technologies of the system. However, there are some unstructured spaces such as road shoulders and slopes pose challenges for trajectory planning of connected-automated vehicle (CAV). Therefore, this paper addresses the problem of CAV trajectory planning affected by unstructured space. Firstly, based on cyber-physical system (CPS), the cyber-physical trajectory planning system (CPTPS) framework was built. A high-precision digital twin CAV is established based on the physical properties and geometric constraints of CAV, and the digital model is mapped to cyber space of the CPTPS. In order to further reduce the energy consumption of the CAV during driving and the time spent from the start to the end, a model was established. Further, based on the sand cat swarm hybrid particle swarm optimization algorithm (SCSHPSO), global path planning for connected-automated vehicles is performed; The vehicle trajectory is smoothed based on a Bezier curve. Finally, the simulation results show the trajectory planning results in unstructured space and two-dimensional plane. Compared to the sand cat swarm optimization (SCSO) algorithm, the fitness function value of the trajectory planned by the SCSHPSO algorithm in unstructured environment has decreased by 6.34%. The simulation results demonstrate the performance of the CPS based trajectory planning scheme for connected-automated vehicles designed in this paper, especially in unstructured environments, where the SCSHPSO algorithm is more competitive

Notes:

Vendor supplied data

Publisher Number:

2024-01-7025

Access Restriction:

Restricted for use by site license