Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems
Song, Ziwei Lv, Mingsong Ren, Tianchi Xue, Chun Jason Wu, Jenming Guan, Nan
Song, Ziwei
Lv, Mingsong
Ren, Tianchi
Xue, Chun Jason
Wu, Jenming
Guan, Nan
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Department
Computer Science
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Conference proceeding
Date
2025
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Language
English
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Abstract
Existing Autonomous Driving Systems (ADS) independently make driving decisions, but they face two significant limitations. First, in complex scenarios, ADS may misinterpret the environment and make inappropriate driving decisions. Second, these systems are unable to incorporate human driving preferences in their decision-making processes. This paper proposes Autoware. Flex, a novel ADS system that incorporates human input into the driving process, allowing users to guide the ADS in making more appropriate decisions and ensuring their preferences are satisfied. Achieving this needs to address two key challenges: (1) translating human instructions, expressed in natural language, into a format the ADS can understand, and (2) ensuring these instructions are executed safely and consistently within the ADS' decision-making framework. For the first challenge, we employ a Large Language Model (LLM) assisted by an ADS-specialized knowledge base to enhance domain-specific translation. For the second challenge, we design a validation mechanism to ensure that human instructions result in safe and consistent driving behavior. Experiments conducted on both simulators and a real-world autonomous vehicle demonstrate that Autoware. Flex effectively interprets human instructions and executes them safely.
Citation
Z. Song, M. Lv, T. Ren, C. J. Xue, J. -M. Wu and N. Guan, "Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems," 2025 IEEE 31st International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Singapore, Singapore, 2025, pp. 1-11, doi: 10.1109/RTCSA66114.2025.00011.
Source
Proceedings of the IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)
Conference
31st IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2025
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Source
31st IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2025
Publisher
IEEE