13 accepted papers
Imitation learning (IL) has been widely adopted for autonomous driving planning because of its data efficiency and stable optimization. Yet IL-based planners often suffer from causal confusion, fitting spurious correlations instead of genuine causal mechanisms, which leads to unreliable planning beh
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…
As the foundation of closed-loop training and evaluation in autonomous driving, traffic simulation still faces two fundamental challenges: covariate shift introduced by open-loop imitation learning and limited capacity to reflect the multimodal behaviors observed in real-world traffic. Although rece…
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
Ensuring safe and socially compliant driving is essential for autonomous vehicle planning. However, one of the significant challenges remains the performance bottleneck caused by interaction uncertainty in complex traffic scenarios. Traditional planning algorithms typically account for all traffic p
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
In autonomous driving, accurately identifying traffic participants that may influence vehicle behavior is crucial for effective system planning. To address this challenge, we propose a fault-tolerant mechanism for detecting action-induced objects, which significantly improves decision-making perform…
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
Motion forecasting is an important component in autonomous driving systems. One of the most challenging problems in motion forecasting is interactive trajectory prediction, whose goal is to jointly forecasts the future trajectories of interacting agents. To this end, we present a large-scale interac…
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