Autonomous car racing is a challenging task, as it requires precise applications of control while the vehicle is operating at cornering speeds. Traditional autonomous pipelines require accurate pre-mapping, localization, and planning which make the task computationally expensive and environment-dependent. Recent works propose use of imitation and reinforcement learning to train end-to-end deep neural networks and have shown promising results for high-speed racing. However, the end-to-end models may be dangerous to be deployed on real systems, as the neural networks are treated as black-box models devoid of any provable safety guarantees. In this work we propose a decoupled approach where an optimal end-to-end controller and a state prediction end-to-end model are learned together, and the predicted state of the vehicle is used to formulate a control barrier function for safeguarding the vehicle to stay within lane boundaries. We validate our algorithm both on a high-fidelity Carla driving simulator and a 1/10-scale RC car on a real track. The evaluation results suggest that using an explicit safety controller helps to learn the task safely with fewer iterations and makes it possible to safely navigate the vehicle on the track along the more challenging racing line.

翻译：自主赛车是一项具有挑战性的任务，因为它要求车辆在弯道行驶状态下实现精确的控制应用。传统的自主驾驶流水线需要精确的预建图、定位和规划，这使得任务计算开销大且依赖环境。近期研究提出利用模仿学习和强化学习训练端到端深度神经网络，并在高速竞速场景中展现出令人鼓舞的结果。然而，端到端模型在实际系统部署时可能存在安全隐患，因为神经网络被视为缺乏可验证安全保证的黑箱模型。本研究提出一种解耦方法，将最优端到端控制器与状态预测端到端模型联合学习，利用预测的车辆状态构建控制屏障函数以保证车辆不偏离车道边界。我们在高保真Carla驾驶模拟器和真实赛道的1/10比例遥控车上验证了算法。评估结果表明，采用显式安全控制器有助于以更少迭代次数安全地学习任务，并使车辆能沿着更具挑战性的竞速轨迹在赛道上安全行驶。