Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bounding box predictions can lead to spurious velocity estimates in tracking and falsely predicted trajectories.
arXiv:2610.02765v1 Announce Type: cross Abstract: Monitoring planned driving trajectories requires accurately estimating the collision likelihood with actors whose motion is itself impacted by the eg...
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
arXiv:2609.21486v1 Announce Type: new Abstract: Multimodal trajectory prediction improves behavioral coverage in end-to-end autonomous driving, but existing methods remain limited by sparse scene rep...
The paper presents Context-Aware Attention-based Gaussian Mixture Models (CAA‑GMM) for predicting vehicular trajectories in complex, uncertain environments. CAA‑GMM models future motion as a probabilistic mixture conditioned on scene context and agent dynamics, using a lightweight attention mechanism to encode inter‑agent interactions and contextual salience. Experiments on nuScenes and Argoverse 2 show competitive or superior accuracy to raster‑based baselines while keeping computational complexity low, and ablation studies confirm the attention module’s importance for robust contextual reasoning.
arXiv:2606. 26661v1 Announce Type: cross Abstract: Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios.
The paper introduces iDriveVLA, a multi‑modal planning framework for autonomous driving that addresses a generation‑evaluation asymmetry by improving candidate trajectory spaces and providing a unified, safety‑aware evaluator. It combines a Safety‑aware Scorer for risk estimation with a VLM‑guided Modulator that adapts weighting to the scene, and employs an oracle‑aligned progressive training strategy. On the NAVSIM v1 leaderboard, iDriveVLA achieves a new state‑of‑the‑art PDMS score of 94.95, surpassing human‑expert performance.
arXiv:2606. 17362v1 Announce Type: cross Abstract: Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent.
The paper audits runtime failure monitors that use a model’s internal representations to predict failures in autonomous driving tasks. Across two tasks—online vectorized map generation with LaneSegNet and end‑to‑end planning with VAD—the authors find that frame‑level errors can be predicted with high AUROC scores using supervised latent probes. However, adding latent features to baseline monitors that use only observable inputs and outputs does not yield statistically significant improvements, suggesting that internal representations may not provide additional predictive value beyond what is already observable.
arXiv:2606. 01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings.
RiskWorld is a risk‑aware world modeling framework that forecasts shared occupancy and selectively replaces planned trajectories in automated driving. It fuses spatial risk fields, temporal actor context, and visual bird’s‑eye‑view features, using flow‑guided evolution to transport occupancy and signed residuals to correct it. In open‑loop planning on nuScenes, RiskWorld achieves the lowest collision rate over a 3‑second horizon and the second‑best average L2 error, running at 11.5 FPS on a single NVIDIA RTX 4090.
arXiv:2606. 15251v1 Announce Type: cross Abstract: Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential for safe autonomous navigation.