According to statistics, the majority of accidents are attributed to driver negligence, especially when a driver intends to lane change or to overtake another vehicle, which is most likely to cause accidents. In addition, overtaking is one of the most difficult and complex functions for the development of autonomous driving technologies because of the dynamic and complicated task involved in the control strategy and electronic control systems, such as steering, throttle, and brake control. This paper proposes a safe overtaking maneuver procedure for an autonomous vehicle based on time to lane crossing (TLC) estimation and the model predictive control scheme. As overtaking is one of the most complex maneuvers that require both lane keeping and lane changing, a vision-based lane-detection system is used to estimate TLC to make a timely and accurate decision about whether to overtake or remain within the lane. Next, to maintain the minimal safe distance and to choose the best timing to overtake, the successive linearization-based model predictive control is employed to derive an optimal vehicle controller, such as throttle, brake, and steering angle control. Simultaneously, it can make certain that the longitudinal acceleration and steering velocity are maintained under constraints to maintain driving safety. Finally, the proposed system is validated by real-world experiments performed on a prototype electric golf cart and executed in real-time on the automotive embedded hardware with limited computational power. In addition, communication between the sensors and actuators as well as the vehicle control unit (VCU) are based on the controller area network (CAN) bus to realize vehicle control and data collection. The experiments demonstrate the ability of the proposed overtaking decision and control strategy to handle a variety of driving scenarios, including a lane-following function when a relative yaw angle exists and an overtaking function when the approaching vehicle has a different lateral velocity.

中文翻译：

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基于车道交叉时间估计和模型预测控制的自主超车系统的实现

据统计，大部分事故都归咎于驾驶员的疏忽，尤其是当驾驶员打算变道或超车时，最容易造成事故。此外，超车是自动驾驶技术发展中最困难、最复杂的功能之一，因为其涉及的控制策略和电子控制系统，如转向、油门和制动控制等，都是动态的、复杂的任务。本文提出了一种基于车道交叉时间（TLC）估计和模型预测控制方案的自主车辆安全超车机动程序。由于超车是需要保持车道和变换车道的最复杂的操作之一，基于视觉的车道检测系统用于估计 TLC，以便及时准确地决定是超车还是保持在车道内。接下来，为了保持最小安全距离并选择最佳超车时机，采用基于连续线性化的模型预测控制来推导最佳车辆控制器，例如油门、制动和转向角控制。同时，可以保证纵向加速度和转向速度保持在约束条件下，以保证行车安全。最后，所提出的系统通过在原型电动高尔夫球车上执行的真实世界实验进行验证，并在计算能力有限的汽车嵌入式硬件上实时执行。此外，传感器和执行器以及车辆控制单元（VCU）之间的通信基于控制器局域网（CAN）总线，实现车辆控制和数据采集。实验证明了所提出的超车决策和控制策略处理各种驾驶场景的能力，包括存在相对偏航角时的车道跟随功能和接近车辆具有不同横向速度时的超车功能。