Lane-Frame Quantum Multimodal Driving Forecasts for the Trajectory of Autonomous Vehicles

Abstract--Trajectory forecasting for autonomous driving must deliver accurate, calibrated multi-modal futures under tight compute and latency constraints. We propose a compact hybrid quantum architecture that aligns quantum inductive bias with road-scene structure by operating in an ego-centric, lane-aligned frame and predicting residual corrections to a kinematic baseline instead of absolute poses. The model combines a transformer-inspired quantum attention encoder (9 qubits), a parameter-lean quantum feedforward stack (64 layers, 1200 trainable angles), and a Fourier-based decoder that uses shallow entanglement and phase superposition to generate 16 trajectory hypotheses in a single pass, with mode confidences derived from the latent spectrum. All circuit parameters are trained with Simultaneous Perturbation Stochastic Approximation (SPSA), avoiding back-propagation through non-analytic components. In the Waymo Open Motion Dataset, the model achieves minADE (minimum A verage Displacement Error) of 1.94 m and minFDE (minimum Final Displacement Error) of 3.56m in the 16 models predicted over the horizon of 2.0 s, consistently outperforming a kinematic baseline with reduced miss rates and strong recall. Ablations confirm that residual learning in the lane frame, truncated Fourier decoding, shallow entanglement, and spectrum-based ranking focus capacity where it matters, yielding stable optimization and reliable multi-modal forecasts from small, shallow quantum circuits on a modern autonomous-driving benchmark. CCURA TE short-horizon trajectory forecasting under uncertainty is a central requirement for autonomous driving. An effective forecaster must reason about multiple plausible futures (e.g., straight vs. turn), remain well-calibrated, and operate under strict latency and compute budgets.