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%.

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具有高效性。通过利用深度学习技术进行决策，此类系统常被视为黑箱，其结果由数据驱动。本文提出PaaS（规划即服务），一种为CARLA仿真环境中的自动驾驶生成局部轨迹规划的通用模块。该方法提交至国际CARLA自动驾驶排行榜——一个在真实交通场景中评估自主代理驾驶能力的平台。我们的方法聚焦于城市复杂街道约束与驾驶员舒适度下Frenet框架中的反应式规划。该规划器通过启发式代价函数生成可行轨迹集，并利用可控驾驶风格因子选择满足安全行驶准则的最优控制路径。PaaS能够为CADL中具有挑战性的交通场景提供充足解决方案。在CADL赛道严格评估中，以驾驶得分指标衡量，本方法在9个提交方案中位列第三。然而，由于重点在于最小化操纵风险并保障乘客安全，我们的违规扣分相关指标较前两名提交方案优势达20%。