9 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
Modern AI algorithms increasingly adopt fused kernels for performance, but implementing them remains complex due to the lack of fine-grained control in existing compilers like Triton. We introduce TileLang, a controllable programming system for fused neural kernels. TileLang provides explicit tile-l…
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
World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data. Existing methods struggle to disentangle ego-vehicle motion (perspective shifts) from scene evolvement (agent interactions), leading to suboptimal predictions. Instead, we propose to sepa…
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 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 well-established modular autonomous driving system is decoupled into different standalone tasks, e.g. perception, prediction and planning, suffering from information loss and error accumulation across modules. In contrast, end-to-end paradigms unify multi-tasks into a fully differentiable framew
Predicting the future occupancy states of the surrounding environment is a vital task for autonomous driving. However current best-performing single-modality methods or multi-modality fusion perception methods are only able to predict uniform snapshots of future occupancy states and require strictly…
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