Summary of Integrating End-to-end and Modular Driving Approaches For Online Corner Case Detection in Autonomous Driving, by Gemb Kaljavesi et al.
Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving
by Gemb Kaljavesi, Xiyan Su, Frank Diermeyer
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper proposes a novel method for online corner case detection in autonomous driving vehicles. The authors integrate an end-to-end approach into a modular system, leveraging the strengths of both approaches to improve situational awareness and detect rare corner cases. In this setup, a primary driving task is handled by the modular system, while an end-to-end network runs in parallel as a secondary one. Disagreements between these systems are then used for corner case detection. The authors implement their method on a real vehicle and evaluate it qualitatively, finding that end-to-end networks can effectively contribute to corner case detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making self-driving cars safer by using artificial intelligence (AI) to detect unusual situations. Right now, there are two main approaches to autonomous driving: one that breaks down the task into smaller parts and another that does everything at once. The authors combine these two methods to create a better way of detecting unexpected events. They tested this approach on a real car and found that it can help make self-driving cars safer. |
