Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Abstract

Autonomous driving is one of the promising technologies to tackle traffic accident and congestion problems nowadays. Even though an autonomous vehicle is operated without humans, it is necessary to reflect the driving characteristics of a human driver. This can increase user acceptance to autonomous driving system, which in turn will improve driving safety because of human occupants' trust in it. In this paper, a combined trajectory planning and tracking algorithm is proposed for the vehicle control. Firstly, traffic environments and driving styles are modeled with the Artificial Potential Field (APF) approach. Secondly, those APF values are integrated into the Model Predictive Control (MPC) design process, which can optimize the trajectories and control outputs. In this way, we add people's driving habits and styles into the controller, so that the controlled vehicle can move under the effects of the traffic environments and human's driving styles. At last, autonomous driving, which reflects two types of human drivers' driving styles (a cautious driving style and an aggressive one), is tested by the simulation experiments in two scenarios (carfollowing and lane-changing). Furthermore, the result demonstrates that the proposed algorithm can reflect driving styles. Accordingly, this novel controller can be utilized in the autonomous vehicle control field.

Research on Car-Following Model considering Driving Style

“…Different behavior characteristics have been used in different studies to identify driving styles. Aljaafreh et al [19] and Chen and Chen [20] selected acceleration and speed of the FV as characteristics to identify driving style, while Li et al [15] chose acceleration and time headway. Gao et al [21] used a group of variables, e.g., relative speed, time headway, and jerk, to reflect the differences in driving styles.…”

“…e limitation of this study is that the speed-change range is limited to the range of 23-29 m/s and is not convincing in other cases. Li et al [15] modeled the traffic environments and driving styles with an artificial potential field (APF). e APF values are used in the MPC model design process.…”

A Security-by-Design Decision-Making Model for Risk Management in Autonomous Vehicles

“…There are some recent studies in the field of ITS that have been applied under many scenarios. For instance, Li et al [26] proposed a combined trajectory planning and tracking algorithm for vehicle control under the effects of the traffic environments and human driving styles. Chen et al, [27] suggested two techniques to improve the stability of the policy model training with as little manual data as possible on endto-end autonomous driving.…”

Four-wheel-drive vehicle trajectory tracking control at joint planning layer

“…Zuo et al (Zuo et al, 2021) considered the cooperative control of local planning and path tracking of intelligent vehicles, and proposed a progressive model predictive control method, and considered traffic lights and moving obstacles through a pseudo-speed planning algorithm, which improved the reliability of the hierarchical algorithm. Li et al (Li et al, 2021) used the artificial potential field method to model the traffic environment and driving style, and integrated the APF value into the MPC trajectory tracking controller to optimize the trajectory and control output, which could reflect the driving style during the control process. Giuseppe et al (Giuseppe et al, 2018) used the Firefly-Algorithm algorithm to optimize model predictive control, so that it could consider constraints such as road boundaries and obstacles in urban environments, and guide the vehicle towards the target point.…”