TY  - JOUR
PY  - 2022//
TI  - Velocity obstacle-based collision avoidance and motion planning framework for connected and automated vehicles
JO  - Transportation research record
A1  - Wang, Shunchao
A1  - Li, Zhibin
A1  - Wang, Bingtong
A1  - Ma, Jingfeng
A1  - Yu, Jingcai
SP  - 748
EP  - 766
VL  - 2676
IS  - 5
N2  - This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.<p />  <p>Language: en</p>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/03611981211070286
ID  - ref1
ER  -