摘要
目前无人驾驶发展迅速,城市运营环境中避障轨迹规划是核心问题之一,如何避免轨迹离散优化过程中可能出现的角碰等状况逐渐成为研究热点。文章比较了轨迹规划中坐标系的优缺点并选取笛卡尔坐标系,提出考虑自车形状的车辆模型,在车辆模型基础上结合新的障碍物边界处理方法进一步排查可能的碰撞危险区域,以起到简化环境信息避免进行碰撞检测、减少规划耗时的效果。结合自车与周围障碍物位置关系,文章提出通过使用运动走廊,结合MinimumSnap最优化理论进行硬约束轨迹优化,最后通过仿真验证证明该方法可行,并且能够有效满足避障场景下安全性和舒适性要求。
At present, unmanned driving is developing rapidly, and obstacle avoidance trajectory planning in the urban operating environment is one of the core issues. How to avoid corner collisions that may occur during the trajectory discrete optimization process has gradually become a research hotspot. This article compares the advantages and disadvantages of the coordinate system in the trajectory planning and selects the Cartesian coordinate system. It proposes a self-vehicle model that considers the shape of the vehicle. Based on the vehicle model, it combines a new obstacle boundary processing method to further investigate possible collisions and dangerous areas to simplify environmental information in order to achieve the effect of avoiding collision detection and reducing time-consuming planning. Combined with the positional relationship between the vehicle and the surrounding obstacles, the article proposes the use of motion corridors, combined with the Minimum Snap optimization theory for hard-constrained trajectory optimization, and finally proves that the method is feasible through simulation verification, and can effectively meet the safety and comfort requirements in obstacle avoidance scenarios.
作者
徐靖贤
XU Jingxian(School of Automobile,Chang'an University,Xi'an 710064,China)
出处
《汽车实用技术》
2023年第4期9-13,共5页
Automobile Applied Technology
关键词
自动驾驶
轨迹规划
运动走廊
贝塞尔曲线
Automatic driving
Trajectory planning
Motion corridor
Bezier curve
