From Correlation to Causation in Lane Change Prediction for Automated Driving: A Causal Explanation Framework
arXiv:2606.15756v1 Announce Type: cross Abstract: Lane-change prediction is a central task in intelligent vehicles, where early maneuver anticipation can support safer decision-making. However, many existing approaches mainly learn statistical associations between observed driving variables and future maneuvers, while overlooking the causal dependencies among the input variables themselves. This limits interpretability, especially when physically related variables such as longitudinal gap, relative longitudinal velocity, and Time-To-Collision (TTC) are treated as independent flat inputs. This
This paper addresses a critical limitation in current AI approaches for autonomous driving by focusing on causal reasoning, a burgeoning area of AI research essential for robust real-world applications.
Improving interpretability and reliability in autonomous driving systems through causal AI is crucial for regulatory approval, public trust, and the safe deployment of self-driving vehicles.
The shift from purely statistical correlations to causal explanations fundamentally enhances the trustworthiness and predictability of AI models in safety-critical applications like lane change prediction.
- · Autonomous Driving Developers
- · AI Safety Researchers
- · Automotive Industry
- · Regulatory Bodies
- · Developers solely relying on correlational AI models
- · Companies with opaque AI driving systems
Automated driving systems will become more reliable and explainable in complex traffic scenarios.
Increased public acceptance and faster regulatory approval for higher levels of autonomous driving.
The methodology could generalize to other safety-critical AI applications, requiring a paradigm shift in AI design across industries.
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Read at arXiv cs.AI