Clothoid-Based Speed Profiler and Control for Autonomous Driving

P. Lima, M. Trincavelli, J. Mårtensson, B. Wahlberg
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引用次数: 22

Abstract

This paper presents a method for optimal speed profile generation in specified clothoid-based paths with known semantic - maximum speed and longitudinal and lateral acceleration - and geometric information. A clothoid can be described using only its kink-points information, i.e. the points defining the start and end of a clothoid. Using the clothoid-based path representation, we formulate the speed profile generation as a convex optimization problem where the objective is to produce a smooth speed that is close to the maximum allowed speed. The vehicle and the road profile define the constraints of the problem. Furthermore, we develop a longitudinal controller by using the speed profiler in a receding-horizon fashion. Thus, we only consider a finite horizon when computing the optimal inputs every sampling time and, in addition, the longitudinal controller also takes into account the newest prediction available from measurements and from the lateral controller. We present simulations that demonstrate the ability of the method to generate safe and feasible speed profiles and the tracking of those by the longitudinal controller. We also study the influence of the clothoid-based path representation in the optimality of the speed profile obtained. We show that we can get a very good suboptimal speed profile approximation with few more points than the kink-points. In addition, we analyze the influence of an acceleration penalization factor in the smoothness of the speed profiler. The higher the acceleration penalization the smoother and the further from the maximum allowed speed is the speed profile.
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基于clothoid的自动驾驶速度分析器与控制
本文提出了一种在已知语义(最大速度、纵向和横向加速度)和几何信息的情况下,在给定的基于梭形线的路径上生成最优速度剖面的方法。一个clodroid可以只用它的扭结点信息来描述,即定义clodroid的起点和终点的点。使用基于clothoid的路径表示,我们将速度剖面生成表述为一个凸优化问题,其目标是产生接近最大允许速度的平滑速度。车辆和道路轮廓定义了问题的约束条件。此外,我们还开发了一种纵向控制器,使用速度剖面仪在后退地平线的方式。因此,在计算每次采样时间的最优输入时,我们只考虑有限的视界,此外,纵向控制器还考虑了从测量和横向控制器中获得的最新预测。我们给出的仿真证明了该方法能够生成安全可行的速度曲线,并由纵向控制器跟踪这些速度曲线。我们还研究了基于梭形线的路径表示对速度曲线最优性的影响。我们证明了我们可以用比扭结点多一点的点得到一个非常好的次优速度轮廓近似。此外,我们还分析了加速度惩罚因子对速度分析器平稳性的影响。加速度惩罚越高,速度轮廓越平滑,距离最大允许速度越远。
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