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：摘要：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。通过使用深度学习技术进行决策，这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），一个用于在CARLA仿真中生成自动驾驶局部轨迹规划的标准化模块。该方法提交至国际CARLA自动驾驶排行榜（CADL），该平台旨在评估自主代理在真实交通场景中的驾驶熟练度。我们的方法聚焦于复杂城市道路约束及驾驶员舒适度下的Frenet框架反应式规划。该规划器生成一组可行轨迹，利用具有可控驾驶风格因子的启发式成本函数选择满足安全行驶准则的最优控制路径。PaaS能为CADL中具有挑战性的交通场景提供充分解决方案。在CADL地图赛道严格评估中，我们的方法在驾驶得分指标上排名第三（共9个提交方案）。然而，由于重点在于最小化机动风险并确保乘客安全，我们的违规罚分相关数据相比前两名提交方案低20%。