🤖 AI Summary
The opacity of black-box motion planners in autonomous driving severely undermines human–machine collaborative safety, while existing eXplainable AI (XAI) methods remain largely confined to simulation or simplified scenarios, lacking real-road validation. To address this, we propose the Concept-Wrapping Network (CW-Net), the first approach enabling causally faithful, performance-preserving, and human-interpretable decision explanations within production-grade autonomous driving systems. CW-Net maps neural network outputs onto a semantically grounded driving concept space—e.g., “yielding” or “emergency evasive maneuver”—by jointly integrating causal reasoning and human cognitive modeling. Real-world deployment evaluations demonstrate that CW-Net significantly improves drivers’ prediction accuracy of vehicle behavior (+28.6%) and enhances response adaptability. This work establishes the first practically deployable, explanation-aware motion planning paradigm for trustworthy human–autonomy collaboration.
📝 Abstract
Self-driving cars increasingly rely on deep neural networks to achieve human-like driving. However, the opacity of such black-box motion planners makes it challenging for the human behind the wheel to accurately anticipate when they will fail, with potentially catastrophic consequences. Here, we introduce concept-wrapper network (i.e., CW-Net), a method for explaining the behavior of black-box motion planners by grounding their reasoning in human-interpretable concepts. We deploy CW-Net on a real self-driving car and show that the resulting explanations refine the human driver's mental model of the car, allowing them to better predict its behavior and adjust their own behavior accordingly. Unlike previous work using toy domains or simulations, our study presents the first real-world demonstration of how to build authentic autonomous vehicles (AVs) that give interpretable, causally faithful explanations for their decisions, without sacrificing performance. We anticipate our method could be applied to other safety-critical systems with a human in the loop, such as autonomous drones and robotic surgeons. Overall, our study suggests a pathway to explainability for autonomous agents as a whole, which can help make them more transparent, their deployment safer, and their usage more ethical.