12 accepted papers
Previous fast–slow system architectures demonstrated that pairing a reactive E2E planner with a deliberative vision-language model (VLM) can address these long-tail scenarios. However, these dual-system models that query the slow module at fixed intervals are computationally inefficient and introduc…
Vision-Language Models (VLMs) have demonstrated significant potential for end-to-end autonomous driving, yet a substantial gap remains between their current capabilities and the reliability necessary for real-world deployment. A critical challenge is their fragility, characterized by hallucinations …
Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce AgentThink , a pioneering unified framewor
3D occupancy prediction (3DOcc) is a rapidly rising and challenging perception task in the field of autonomous driving. Existing 3D occupancy networks (OccNets) are both computationally heavy and label-hungry. In terms of model complexity, OccNets are commonly composed of heavy Conv3D modules or tra
Accurate localization plays an important role in high-level autonomous driving systems. Conventional map matching-based localization methods solve the poses by explicitly matching map elements with sensor observations, generally sensitive to perception noise, therefore requiring costly hyperparamete
Accurate spatial and motion understanding is critical for autonomous driving systems. While object-level perception models excel in structured environments, they struggle with open-set categories and often lack precise geometric representation. Occupancy-based, class-agnostic methods offer better sc
The vehicle dynamics model serves as a vital component of autonomous driving systems, as it describes the temporal changes in vehicle state. Traditional physics-based methods employ mathematical formulae to model vehicle dynamics, but they are unable to adequately describe complex vehicle systems du
High-precision vehicle localization with commercial setups is a crucial technique for high-level autonomous driving tasks. As a newly emerged approach, monocular localization in LiDAR map achieves promising balance between cost and accuracy, but estimating pose by finding correspondences between suc
The self-driving application requires accurate 3D object detection as it is essential in several tasks, such as path and motion planning. However, up until this point, fusion-based detectors with cameras and LiDAR sensors have always been inferior to LiDAR-only detectors. This can be attributed to t
The traditional HD map production based on mapping vehicles equipped with expensive sensors (e.g. LiDAR) struggles to keep the frequently changing HD maps up-to-date, thus calling for great research efforts in the HD map reconstruction using low-cost vision sensors. However, most existing works in t
Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict regio…
Loop closure detection (LCD) is a crucial technique for robots, which can correct accumulated localization errors after long time explorations. In this paper, we propose a robust LCD algorithm based on Bag of SuperPoints and graph verification. The system first extracts interest points and feature d…