A DRL policy learns racing controls from depth spectral distributions using a non-geometric physics-informed reward, achieving 12% better performance than humans on out-of-distribution tracks with under 1% of baseline computation.
Minimum curvature trajectory planning and control for an autonomous race car,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A bi-level game-theoretic optimal control plus reinforcement learning framework enables competitor-aware energy management and pit-stop scheduling that exploits aerodynamic drafting in simulated electric endurance races.
Neural network predicts raceline offsets from local track geometry using Formula 1 data to initialize minimum-time optimal control, accelerating solver convergence on 17 tracks while preserving lap times.
citing papers explorer
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Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing
A DRL policy learns racing controls from depth spectral distributions using a non-geometric physics-informed reward, achieving 12% better performance than humans on out-of-distribution tracks with under 1% of baseline computation.
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Competitor-aware Race Management for Electric Endurance Racing
A bi-level game-theoretic optimal control plus reinforcement learning framework enables competitor-aware energy management and pit-stop scheduling that exploits aerodynamic drafting in simulated electric endurance races.
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Efficient Trajectory Optimization for Autonomous Racing via Formula-1 Data-Driven Initialization
Neural network predicts raceline offsets from local track geometry using Formula 1 data to initialize minimum-time optimal control, accelerating solver convergence on 17 tracks while preserving lap times.