Summary of Decictor: Towards Evaluating the Robustness Of Decision-making in Autonomous Driving Systems, by Mingfei Cheng et al.
Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems
by Mingfei Cheng, Yuan Zhou, Xiaofei Xie, Junjie Wang, Guozhu Meng, Kairui Yang
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE); Robotics (cs.RO); Software Engineering (cs.SE)
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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 The proposed paper focuses on evaluating the robustness of Autonomous Driving System (ADS) path-planning decisions (PPDs), specifically assessing whether an ADS can maintain optimal PPDs after minor environmental changes. The authors highlight the importance of evaluating non-safety-critical performance, alongside safety, to ensure intelligent and risk-reduced autonomous vehicles. They propose a method called Decictor for generating non-optimal decision scenarios (NoDSs) by implementing conservative modifications and consistency checks. This approach integrates spatial and temporal dimensions of the AV’s movement through feedback metrics. The evaluation on Baidu Apollo, an open-source ADS, demonstrates the effectiveness of Decictor in detecting non-optimal PPDs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous vehicles need to make smart decisions on the road. Right now, people are mostly worried about safety, but it’s also important for these vehicles to make good choices and avoid problems. The problem is that nobody has a clear way to check if these decisions are really the best ones. This paper proposes a new method called Decictor to test how well an autonomous driving system makes decisions in different situations. It works by making small changes to the environment and checking if the vehicle’s decision stays optimal or not. The results show that this approach can effectively detect when the vehicle is making suboptimal decisions. |
