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首页> 外文期刊>Fortschritte der Physik >Vehicle Risk Assessment and Control for Lane-Keeping and Collision Avoidance at Low-Speed and High-Speed Scenarios
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Vehicle Risk Assessment and Control for Lane-Keeping and Collision Avoidance at Low-Speed and High-Speed Scenarios

机译：车辆风险评估和控制在低速和高速场景下的巷道保持和碰撞避免

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This paper examines a symbolic numerical approach to optimize a vehicles track for autonomous driving and collision avoidance. The new approach uses the classical cost function definition incorporating the essential aspects of the dynamic state of the vehicle as position, orientation, time sampling, and constraints on slip angles of tires. The optimization processes minimize the cost function and simultaneously determine the optimal track by varying steering and breaking amplitudes. The current velocity of the vehicle is limited to a maximal velocity, thus, allowing a stable search of the optimal track. The parametric definition of obstacles generates a flexible environment for low and high speed simulations. The minimal number of influential optimization variables guarantees a stable and direct generation of optimal results. By the current new approach to control a vehicle on an optimal track, we are able to autonomously move the vehicle on an arbitrary track approximated by low order polynomials. The optimization approach is also able to deal with a variety of different obstacles and the corresponding optimal smooth obstacle path. The computations demonstrate the effective control of a four wheel vehicle in normal operation and exceptional obstacle avoidance with continuously differentiable obstacle avoidance tracks. Simulation tests are done using vehicles velocities of 3, 6, 7.6, 10, 12, and 18 m/s. At higher vehicles velocities, a mathematical-only approach is not sufficient and a mechanical intervention for tires is needed as a complimentary part to control the slip angle. The results shows that the cost function reached a considerably high average convergence-to-zero rate success in most of the tested scenarios.
机译：本文介绍了一种符号数字方法，以优化自主驾驶和碰撞避免的车辆轨道。新方法使用经典成本函数定义，其包括车辆动态状态的基本方面作为轮胎滑动角度的位置，方向，时间采样和约束。优化过程使成本函数最小化，并通过不同的转向和断裂幅度同时确定最佳轨道。因此，车辆的当前速度限于最大速度，从而允许稳定地搜索最佳轨道。障碍的参数定义为低速和高速模拟产生了灵活的环境。影响最大数量的影响力优化变量保证了稳定和直接的最佳结果。通过目前在最佳轨道上控制车辆的新方法，我们能够在近似低阶多项式近似的任意轨道上自动移动车辆。优化方法还能够处理各种不同的障碍物和相应的最佳光滑障碍路径。计算展示了在正常操作中对四轮车辆的有效控制，以及具有连续可差的障碍物避免轨道的特殊障碍物避免。使用3,6,7,6,10,12和18m / s的车辆速度进行仿真试验。在较高的车辆速度下，仅数学方法是不够的，并且需要对轮胎的机械干预作为控制滑移角来进行互补部分。结果表明，在大多数测试场景中，成本函数在大多数测试方案中达到了相当高的平均收敛到零率成功。

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