PaaS: Planning as a Service for reactive driving in CARLA Leaderboard

TL;DR

This paper introduces PaaS, a local trajectory planning module for autonomous driving in CARLA, focusing on reactive, safe, and comfortable navigation in complex urban scenarios, and demonstrates competitive performance on the CADL leaderboard.

Contribution

We propose PaaS, a novel reactive trajectory planner using heuristic cost functions for autonomous driving in CARLA, emphasizing safety and driving style customization.

Findings

Ranked 3rd in CADL Map Track with a high driving score.

Achieved 20% better infraction penalty compared to top submissions.

Effective in challenging traffic scenarios with safe maneuvering.

Abstract

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.