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

• URL: http://arxiv.org/abs/2511.17675v1
• Date: Fri, 21 Nov 2025 07:00:07 GMT
• Title: Lane-Frame Quantum Multimodal Driving Forecasts for the Trajectory of Autonomous Vehicles
• Authors: Navneet Singh, Shiva Raj Pokhrel,
• Abstract summary: Tray forecasting for autonomous driving must deliver accurate, multi-modal calibrated forecasts under tight compute and latency constraints.<n>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.<n>In the Open Motion dataset, the model achieves minADE of SI1.94m and minFDE of SI3.56m, consistently outperforming a kinematic baseline with reduced miss rates and strong recall.
• Score: 6.137648436139678
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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, ${\sim}1200$ 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 backpropagation through non-analytic components. In the Waymo Open Motion Dataset, the model achieves minADE (minimum Average Displacement Error) of \SI{1.94}{m} and minFDE (minimum Final Displacement Error) of \SI{3.56}{m} in the $16$ models predicted over the horizon of \SI{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.

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