To ensure that autonomous vehicles satisfy the requirements of the traffic environment, vehicle driving ability, and desired driver experience during obstacle avoidance, this paper proposes a trajectory planner that considers three aspects: driving passability, regional safety, and driving acceptance. Multiresolution state lattices and Bézier curve fitters are applied to a state lattice framework to generate candidate obstacle avoidance trajectory clusters. Trajectory evaluation is then carried out in the above three aspects by using trajectory passability, safety and driver behavior proximity, and a trajectory evaluation function is designed to evaluate and screen trajectory clusters. The trajectory passability is checked by the vehicle motion capability set, which is established based on the vehicle dynamics model. The trajectory safety is evaluated by the potential field function between the fitted trajectory and the vehicle driving environment boundary with consideration of the inevitable collision state. The parameters of the vehicle motion state for the fitted trajectory are matched with the driving data of real drivers with different driving styles to evaluate the proximity between the trajectory and driver behavior. The rationality and effectiveness of different driving styles of trajectory planners are verified by vehicle tests under different vehicle velocities and different obstacle disturbances.

为确保自动驾驶汽车在避障过程中满足交通环境、车辆驾驶能力和期望驾驶体验的要求，本文提出了一种考虑三个方面的轨迹规划器：驾驶通过性、区域安全性和驾驶接受度。多分辨率状态格和贝塞尔曲线拟合器应用于状态格框架以生成候选避障轨迹簇。然后利用轨迹通过性、安全性和驾驶员行为接近度对上述三个方面进行轨迹评价，设计轨迹评价函数对轨迹聚类进行评价和筛选。轨迹通过性由基于车辆动力学模型建立的车辆运动能力集来检验。轨迹安全性由拟合轨迹与车辆行驶环境边界之间的势场函数评估，并考虑不可避免的碰撞状态。拟合轨迹的车辆运动状态参数与具有不同驾驶风格的真实驾驶员的驾驶数据相匹配，以评估轨迹与驾驶员行为之间的接近度。通过不同车速和不同障碍物扰动下的车辆试验，验证了轨迹规划器不同驾驶方式的合理性和有效性。拟合轨迹的车辆运动状态参数与具有不同驾驶风格的真实驾驶员的驾驶数据相匹配，以评估轨迹与驾驶员行为之间的接近度。通过不同车速和不同障碍物扰动下的车辆试验，验证了轨迹规划器不同驾驶方式的合理性和有效性。拟合轨迹的车辆运动状态参数与具有不同驾驶风格的真实驾驶员的驾驶数据相匹配，以评估轨迹与驾驶员行为之间的接近度。通过不同车速和不同障碍物扰动下的车辆试验，验证了轨迹规划器不同驾驶方式的合理性和有效性。