8 accepted papers
The low-light conditions are challenging to the vision-centric perception systems for autonomous driving in the dark environment. In this paper, we propose a new benchmark dataset (named DarkDriving) to investigate the low-light enhancement for autonomous driving. The existing real-world low-light e…
We introduce DriveAgent, a modular multi-agent autonomous driving framework that leverages large language model (LLM) reasoning combined with multimodal sensor fusion for autonomous driving. DriveAgent orchestrates specialized agents operating on camera, Light Detection and Ranging (LiDAR), Inertial…
End-to-end driving frameworks, which directly map raw sensor data to vehicle control commands, have shown remarkable potential. However, their performance often deteriorates in sparse-critical scenarios, where rare but safety-sensitive events occur. To address this problem, we propose Explorer-Exper
With the advancement of connected and automated vehicles (CAVs), achieving accurate vehicle trajectory prediction and optimal control has become a critical challenge for improving the efficiency and safety of mixed traffic flow. However, due to the complex dynamic interactions between CAVs and human…
Adverse weather conditions—such as rain, fog, and snow—significantly degrade LiDAR point cloud quality, causing substantial performance deterioration in detection models trained on clean data. To address this, we propose LTDNet, a novel point cloud quality improvement net-work that restores degraded
We introduce DriveAgent, a modular multi-agent autonomous driving framework that leverages large language model (LLM) reasoning combined with multimodal sensor fusion for autonomous driving. DriveAgent orchestrates specialized agents operating on camera, Light Detection and Ranging (LiDAR), Inertial
Transformer-based methods have achieved remarkable performance in event-based object detection, owing to the global modeling ability. However, they neglect the influence of non-event and noisy regions and process them uniformly, leading to high computational overhead. To mitigate computation cost, s…
Recently, some methods estimate depth maps by fusing several adjacent single-view depth probabilities. They have achieved promising performance in multi-view inconsistent areas, such as texture-less surfaces, reflective surfaces, and moving objects. However, these methods involve two new problems: t