MPCFormer explicitly models multi-vehicle social interaction dynamics via physics-informed discrete state-space and Transformer-learned coefficients, yielding 0.86m ADE over 5s and 94.67% planning success with near-zero collisions in closed-loop tests.
Safe, efficient, and comfortable veloc ity control based on reinforcement learning for autonomous driving,
2 Pith papers cite this work. Polarity classification is still indexing.
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AACC combines online IOC for driving style identification with a Stackelberg game planner to proactively protect right-of-way against cut-ins, reporting up to 79.8% safety gains in simulation.
citing papers explorer
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MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
MPCFormer explicitly models multi-vehicle social interaction dynamics via physics-informed discrete state-space and Transformer-learned coefficients, yielding 0.86m ADE over 5s and 94.67% planning success with near-zero collisions in closed-loop tests.
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Anti-bullying Adaptive Cruise Control: A proactive right-of-way protection approach
AACC combines online IOC for driving style identification with a Stackelberg game planner to proactively protect right-of-way against cut-ins, reporting up to 79.8% safety gains in simulation.