End-to-end deep learning approaches has been proven to be efficient in autonomous driving and robotics. By using deep learning techniques for decision-making, those systems are often referred to as a black box, and the result is driven by data. In this paper, we propose PaaS (Planning as a Service), a vanilla module to generate local trajectory planning for autonomous driving in CARLA simulation. Our method is submitted in International CARLA Autonomous Driving Leaderboard (CADL), which is a platform to evaluate the driving proficiency of autonomous agents in realistic traffic scenarios. Our approach focuses on reactive planning in Frenet frame under complex urban street's constraints and driver's comfort. The planner generates a collection of feasible trajectories, leveraging heuristic cost functions with controllable driving style factor to choose the optimal-control path that satisfies safe travelling criteria. PaaS can provide sufficient solutions to handle well under challenging traffic situations in CADL. As the strict evaluation in CADL Map Track, our approach ranked 3rd out of 9 submissions regarding the measure of driving score. However, with the focus on minimizing the risk of maneuver and ensuring passenger safety, our figures corresponding to infraction penalty dominate the two leading submissions for 20 percent.

翻译：端到端深度学习方法已被证明在自动驾驶和机器人领域中具有高效性。通过利用深度学习技术进行决策，这些系统常被视为黑盒，其输出结果由数据驱动。本文提出PaaS（规划即服务），一种为CARLA仿真环境中自动驾驶生成局部轨迹规划的简易模块。我们的方法提交至国际CARLA自动驾驶排行榜，该平台旨在评估自动驾驶智能体在真实交通场景中的驾驶熟练度。本方法聚焦于在城市复杂街道约束及驾驶员舒适性条件下，基于Frenet框架进行反应式规划。该规划器生成一组可行轨迹，并利用启发式代价函数与可调控的驾驶风格因子，选择满足安全行驶准则的最优控制路径。PaaS能够提供充分解决方案，以应对CARLA自动驾驶排行榜中具有挑战性的交通场景。在排行榜地图赛道的严格评估中，本方法在驾驶得分指标上位列9项提交中的第3名。然而，由于重点在于最小化机动风险并保障乘客安全，本方法对应违规惩罚的指标相较于前两名提交高出20%。