Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910432329990144 |
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| author | Zhang, Weize Elmahgiubi, Mohammed Rezaee, Kasra Khamidehi, Behzad Mirkhani, Hamidreza Arasteh, Fazel Li, Chunlin Kaleem, Muhammad Ahsan Corral-Soto, Eduardo R. Sharma, Dhruv Cao, Tongtong |
| author_facet | Zhang, Weize Elmahgiubi, Mohammed Rezaee, Kasra Khamidehi, Behzad Mirkhani, Hamidreza Arasteh, Fazel Li, Chunlin Kaleem, Muhammad Ahsan Corral-Soto, Eduardo R. Sharma, Dhruv Cao, Tongtong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge Zhang, Weize Elmahgiubi, Mohammed Rezaee, Kasra Khamidehi, Behzad Mirkhani, Hamidreza Arasteh, Fazel Li, Chunlin Kaleem, Muhammad Ahsan Corral-Soto, Eduardo R. Sharma, Dhruv Cao, Tongtong Artificial Intelligence 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. |
| title | Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.01394 |