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

TL;DR

This paper introduces a compact hybrid quantum model for autonomous vehicle trajectory prediction that outperforms classical baselines by leveraging quantum attention, residual learning in a lane frame, and Fourier decoding.

Contribution

It presents a novel quantum architecture combining attention, residuals, and Fourier decoding for multi-modal forecasting in autonomous driving, trained with SPSA.

Findings

Achieves minADE of 1.94 m and minFDE of 3.56 m on Waymo dataset.

Outperforms classical kinematic baselines in accuracy and recall.

Uses shallow quantum circuits for stable, reliable multi-modal forecasts.

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.