This paper presents a novel cooperative trajectory planning approach for semi-autonomous driving. The machine interacts with the driver at the decision level and the trajectory generation level. To minimize conflicts between the machine and the human, the trajectory planning problem is decomposed into a high-level behavior decision-making problem and a low-level trajectory planning problem. The approach infers the driver’s behavioral semantics according to the driving context and the driver’s input. The trajectories are generated based on the behavioral semantics and driver’s input. The feasibility of the proposed approach is validated by real vehicle experiments. The results prove that the proposed human–machine cooperative trajectory planning approach can successfully help the driver to avoid collisions while respecting the driver’s behavior.

This paper proposes a maneuver evaluation approach to enhance driving safety by providing decision aids. Based on the deliberative understanding of environmental semantic information, the proposed algorithm evaluates the risk of candidate driving maneuvers, including aggressive lane-change maneuver, passive lane-change maneuver, lane-change abort maneuver, and lane-keeping maneuver. The effectiveness of the proposed method is validated through various simulation experiments. The experimental results demonstrate that the proposed approach is capable of efficiently evaluating the feasibility and the cost of each candidate driving maneuver. The approach also provides suggestions on how to adjust the speed, and when to initiate a lane-change maneuver.

It is of utmost importance for advanced driver assistance systems to evaluate the risk of the current situation and make continuous decisions about what kind of evasive maneuver can be initiated. The purpose of this paper is to establish efficient indicators to evaluate the risk of candidate driving maneuvers for a human-in-the-loop vehicle. A novel safe driving envelope generation method is proposed, which takes various constraints into consideration, including the human operation, vehicle motion limits, and collision avoidance with road boundary and obstacles. The efficiency of the proposed method is validated by simulation experiments and real vehicle tests. The results show that the feasibility of candidate driving maneuvers can be efficiently determined by computing the driving envelope, and the proposed driving envelope method can be easily implemented for real-time applications.