Summary of Autoware.flex: Human-instructed Dynamically Reconfigurable Autonomous Driving Systems, by Ziwei Song et al.
Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems
by Ziwei Song, Mingsong Lv, Tianchi Ren, Chun Jason Xue, Jen-Ming Wu, Nan Guan
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
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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 A novel Autonomous Driving System (ADS) called Autoware.Flex is proposed to address two limitations of existing ADS: misinterpretation in complex scenarios and inability to incorporate human driving preferences. The system translates natural language human input into a format the ADS can understand, using a Large Language Model (LLM) assisted by an ADS-specialized knowledge base. A validation mechanism ensures safe and consistent execution of human instructions within the ADS’ decision-making framework. Experimental results demonstrate effective interpretation and safe execution of human inputs on simulators and real-world autonomous vehicles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new kind of self-driving car system is being developed to make better decisions with human help. The system, called Autoware.Flex, can understand what humans want it to do by translating words into a format the computer can use. This helps ensure that the car makes safe and consistent choices, following human preferences. The developers tested this system on simulators and real-world cars, showing that it works well. |
Keywords
» Artificial intelligence » Knowledge base » Large language model
