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arXiv · 2405.01394

Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge

Weize Zhang·Mohammed Elmahgiubi·Kasra Rezaee·Behzad Khamidehi·Hamidreza Mirkhani·Fazel Arasteh·Chunlin Li·Muhammad Ahsan Kaleem·Eduardo R. Corral-Soto·Dhruv Sharma·Tongtong Cao
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Abstract

In this paper we present the architecture of the Kyber-E2E submission to the map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first place. We employed a modular architecture for our solution consists of five main components: sensing, localization, perception, tracking/prediction, and planning/control. Our solution leverages state-of-the-art language-assisted perception models to help our planner perform more reliably in highly challenging traffic scenarios. We use open-source driving datasets in conjunction with Inverse Reinforcement Learning (IRL) to enhance the performance of our motion planner. We provide insight into our design choices and trade-offs made to achieve this solution. We also explore the impact of each component in the overall performance of our solution, with the intent of providing a guideline where allocation of resources can have the greatest impact.

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Weize Zhang, Mohammed Elmahgiubi, Kasra Rezaee, Behzad Khamidehi, Hamidreza Mirkhani, Fazel Arasteh, Chunlin Li, Muhammad Ahsan Kaleem, Eduardo R. Corral-Soto, Dhruv Sharma, Tongtong Cao. 2024-03-21. Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge. https://arxiv.org/abs/2405.01394

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