Neural network based planners have shown great promises in improving performance and task success rate in autonomous driving. However, it is very challenging to ensure safety of the system with learning enabled components, especially in dense and highly interactive traffic environments. In this work, we propose a neural network based lane changing planner framework that can ensure safety while sustaining system efficiency. To prevent too conservative planning, we assess the aggressiveness and identify the driving behavior of surrounding vehicles, then adapt the planned trajectory for the ego vehicle accordingly. The ego vehicle can proceed to change lanes if a safe evasion trajectory exists even in the worst case, otherwise, it can hesitate around current lateral position or return back to the original lane. We also quantitatively demonstrate the effectiveness of our planner design and its advantage over other baselines through extensive simulations with diverse and comprehensive experimental settings.
翻译:以神经网络为基础的规划者在提高自动驾驶的性能和任务成功率方面表现出了巨大的希望,然而,确保系统安全,使用学习功能的部件,特别是在密集和高度互动的交通环境中,是非常具有挑战性的;在这项工作中,我们提议一个基于神经网络的车道改变计划框架,既能确保安全,又能保持系统效率;为了防止过于保守的规划,我们评估周围车辆的侵略性并查明其驾驶行为,然后相应调整自我驾驶车辆的计划轨迹;如果即使在最坏的情况下也存在安全规避轨迹,自我车辆可以改变车道,否则,它可以对横向位置犹豫不决,或者返回原车道;我们还通过广泛模拟各种全面试验环境,从数量上表明我们的计划设计及其相对于其他基线的优势。