Autonomous vehicles allow utilisation of new optimal driving approaches that increase vehicle safety by combining optimal all-wheel braking and steering even at the limit of tyre–road friction. One important case is an avoidance manoeuvre that, in previous research, for example, has been approached by different optimisation formulations. An avoidance manoeuvre is typically composed of an evasive phase avoiding an obstacle followed by a recovery phase where the vehicle returns to normal driving. Here, an analysis of the different aspects of the recovery phase is presented, and a subsequent formulation is developed in several steps based on theory and simulation of a double lane-change scenario. Each step leads to an extension of the optimisation criterion. Two key results are a theoretical redundancy analysis of wheel-torque distribution and the subsequent handling of it. The overall contribution is a general treatment of the recovery phase in an optimisation framework, and the method is successfully demonstrated for three different formulations: lane-deviation penalty, minimum time, and squared lateral-error norm.

自动驾驶汽车允许利用新的最佳驾驶方法，即使在轮胎-道路摩擦的极限下，也可以通过结合最佳全轮制动和转向来提高车辆安全性。一个重要的案例是回避策略，例如，在以前的研究中，已经通过不同的优化公式来处理这种策略。回避机动通常由避开障碍物的规避阶段和随后的车辆恢复正常驾驶的恢复阶段组成。在这里，对恢复阶段的不同方面进行了分析，并基于双车道变换场景的理论和模拟分几个步骤开发了后续公式。每一步都会导致优化标准的扩展。两个关键结果是车轮扭矩分布的理论冗余分析及其后续处理。总体贡献是在优化框架中对恢复阶段的一般处理，并且该方法成功地证明了三种不同的公式：车道偏离惩罚、最小时间和平方横向误差范数。