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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.