arXiv:2512.24075v5 Announce Type: replace Abstract: Early lane-change intention prediction is essential for autonomous driving and ADAS, but it remains challenging because lane-changing behavior depends on evolving traffic risk, surrounding-vehicle interactions, and target-lane feasibility rather than only instantaneous vehicle states. This study proposes an evolutionary physics-informed temporal fusion framework for three-class lane-change intention prediction, including left lane change, right lane change, and no lane change. Instead of using static physics-informed variables alone, the prop
Advances in AI, particularly in temporal fusion and physics-informed models, are enabling more sophisticated predictions necessary for autonomous systems.
Improved lane-change prediction is critical for the safety and widespread adoption of autonomous driving and advanced driver-assistance systems, directly addressing a core challenge in real-world performance.
The ability of AI systems to predict complex, evolving human-like driving intentions with greater accuracy, moving beyond instantaneous states to dynamic contextual factors.
- · Autonomous vehicle manufacturers
- · ADAS developers
- · AI software providers
- · Transportation sector
- · Traditional vehicle manufacturers (lacking AI integration)
- · Human error-prone driving incident rates
Enhances the reliability and safety of autonomous vehicles in complex traffic scenarios.
Accelerates the regulatory approval and public acceptance of fully autonomous driving technologies.
Potentially reduces traffic accidents and congestion, significantly altering urban planning and infrastructure needs.
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Read at arXiv cs.LG