Although autonomous vehicles (AV) have been rapidly developed, their control technology is not sufficiently mature for daily use, yet not human-centred enough. Some studies regarding trajectory planning are overly conservative and the vehicle avoids obstacles in an unhuman-like trajectory which causes discomfort to passengers; meanwhile, other studies are overly simplistic, the transport scenario, and vehicle trajectory are disregarded. The potential field (PF) algorithm is one of the most frequently used methods for the trajectory planning of AVs; however, most studies regarding the PF algorithm do not consider driving comfort and smoothness. This paper introduces optimised human-centred dynamic trajectory planning for AVs. The PF algorithm is implemented in a vehicle simulation model, which is integrated with model predictive control (MPC). The reference path is planned by PF algorithm and improved by MPC. The human-centred AV control is proposed in a simulation environment. The proposed planning method achieves a trade-off between safety, driving comfort, and driving smoothness and is validated with several driving simulation scenarios.
|Journal||Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering|
|Early online date||23 Feb 2022|
|Publication status||Early online date - 23 Feb 2022|
Bibliographical noteFunding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is partially supported by Sichuan Provincial Key Laboratory of Vehicle Measurement, Control and Safety (QCCK2020-001).
© IMechE 2022.
- Autonomous vehicle
- model predictive control
- potential field algorithm
- trajectory planning
ASJC Scopus subject areas
- Aerospace Engineering
- Mechanical Engineering