A novel driving-behavior-oriented method is proposed in this paper for improving trajectory planning performance of autonomous vehicle driving on urban structural road. Differ from the irregularity and unpredictability of escaping a maze or travelling on off-road, the driving on road emphasizes more on the compliance of road traffic rules and the satisfaction of passenger comfort rather than purely pursuing the shortest route or the shortest time. Therefore, the driving-behavior-oriented framework is employed in trajectory planning, which divides trajectory into lane change, turn and U-turn, according to the basic traffic rules and the daily behaviors of drivers. The presented approach mainly includes basic path planning, fast-bias RRT path planning and velocity planning. The basic path planning consists of lane change, turn and U-turn behaviors, which generates smooth path with continuous curvature. In order to ensure the completeness of the programming algorithm, a fast-bias RRT (FB-RRT) algorithm is embedded. As guiding by the driving behavior, normal random, goal-bias and Gaussian sampling strategies are fused to form FB-RRT, which could make the best use of the basic path planning and reduce the randomness of node’s extension to save the computation time. After collision-free path generating, cubic polynomial curve is employed to schedule velocity profile for coping with vehicle stability requirements, actuator constraints and comfort conditions. The planner has been tested in simulation and a real vehicle in various typical scenarios. Test results illustrate that the presented method could generate a trajectory with controllable extrema of curvature as well as with continuous and smooth enough curvature. Besides, generated trajectory has short length, high success rate (no less than 80% average success rate in complex environment) and real time (the average period is less than 100 ms). Moreover, the velocity profile meets the vehicle stability requirements, actuator constraints, and comfort conditions.
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