Summary of Analysis Of a Modular Autonomous Driving Architecture: the Top Submission to Carla Leaderboard 2.0 Challenge, by Weize Zhang et al.
Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge
by 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
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the winning submission to the CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, a modular architecture consisting of five components: sensing, localization, perception, tracking/prediction, and planning/control. The solution leverages state-of-the-art language-assisted perception models to improve the planner’s reliability in complex traffic scenarios. Open-source driving datasets and Inverse Reinforcement Learning (IRL) enhance the motion planner’s performance. The paper provides insights into design choices and trade-offs made to achieve this solution, as well as the impact of each component on overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how a team created an autonomous driving system that won a big competition. They divided their system into five parts: sensing the environment, figuring out where they are, recognizing what’s around them, predicting what will happen next, and making decisions about what to do. To help make better decisions, they used special language models that can understand human-made language. They also used training data from open-source datasets and a technique called Inverse Reinforcement Learning (IRL) to improve their system. The paper explains how the team made these choices and how each part of the system helped them win the competition. |
Keywords
» Artificial intelligence » Reinforcement learning » Tracking
