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:2509.13577v3 Announce Type: replace-cross Abstract: Trustworthy trajectory prediction grounds autonomous vehicle (AV) safety, yet deployed models inevitably face out-of-distribution (OOD) scene...
arXiv:2606. 04656v1 Announce Type: cross Abstract: Object detection is a safety-critical component of autonomous driving.
arXiv:2607. 08391v1 Announce Type: cross Abstract: Making tradeoffs between execution latency and result utility (i.
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.
The paper introduces OccLinker, a lightweight plugin for vision‑based occupancy networks that reduces flickering by efficiently merging historical static and motion cues with current features via a dual cross‑attention mechanism. It generates correction components to refine base network predictions and proposes a new temporal consistency metric to quantify flickering. Experiments on two benchmark datasets show that OccLinker improves performance with minimal computational overhead while effectively diminishing flickering artifacts.
Recent advancements in LiDAR-only 3D object detection have demonstrated improved detection accuracy over benchmark datasets. However, the adversarial robustness of these models remains untested.
arXiv:2307. 06647v4 Announce Type: replace-cross Abstract: We propose DeepIPCv2, an end-to-end autonomous driving framework that integrates LiDAR-based environmental perception with command-specific control learning.
arXiv:2211.04340v2 Announce Type: replace Abstract: Autonomous driving systems must be capable of making quick decisions based on the perceived environment and specific driving conditions. Perception...
arXiv:2609.21000v1 Announce Type: cross Abstract: Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustne...
The paper introduces a multi‑modal late‑fusion perception pipeline for object detection and tracking in autonomous racing. It combines independent detections from cameras, LiDARs, and RADARs to produce timely and robust state estimates of surrounding vehicles. The tracking framework compensates for detection delays and incorporates vehicle dynamics and track layout knowledge, and its effectiveness is confirmed through real‑world experiments in diverse critical scenarios.
The paper introduces FlexDepth, a family of self‑supervised monocular depth estimation models designed for robust driving perception. FlexDepth uses a two‑stage static‑dynamic decoupled training strategy and a Scale‑Driven Decoder that selects components based on scale size, enabling efficient feature fusion and high‑precision depth maps. Experiments on driving benchmarks show state‑of‑the‑art performance across arbitrary scales with minimal computational cost, with the smallest model (Flex‑Nano) achieving 37.6 FPS on mobile devices.
SARFusion introduces a scene-aware routing approach for camera‑LiDAR 3D object detection, decoupling object‑query decoding into separate camera, LiDAR, and fusion branches. By estimating a global scene reliability prior and incorporating object‑level evidence, each query is routed to the most suitable branch, reducing cross‑modal interference. The method achieves strong performance on the nuScenes test set (72.5 mAP, 74.4 NDS) and demonstrates robustness to sensor corruptions and environmental changes.