6 papers
PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving
Kang Ding, Zhigui Lin, Hongsong Wang +5
This letter presents PrismAD, a decoupled end-to-end autonomous driving framework based on a Semantic Mixture-of-Planners. Existing planners usually aggregate heterogeneous scene t…
SAMoE-VLA: A Scene Adaptive Mixture-of-Experts Vision-Language-Action Model for Autonomous Driving
Zihan You, Hongwei Liu, Chenxu Dang +4
Recent advances in Vision-Language-Action (VLA) models have shown promising capabilities in autonomous driving by leveraging the understanding and reasoning strengths of Large Lang…
CoopDETR: A Unified Cooperative Perception Framework for 3D Detection via Object Query
Zhe Wang, Shaocong Xu, Xucai Zhuang +5
Cooperative perception enhances the individual perception capabilities of autonomous vehicles (AVs) by providing a comprehensive view of the environment. However, balancing percept…
RenderWorld: World Model with Self-Supervised 3D Label
Ziyang Yan, Wenzhen Dong, Yihua Shao +8
End-to-end autonomous driving with vision-only is not only more cost-effective compared to LiDAR-vision fusion but also more reliable than traditional methods. To achieve a economi…
Idempotence and Perceptual Image Compression
Tongda Xu, Ziran Zhu, Dailan He +8
Idempotence is the stability of image codec to re-compression. At the first glance, it is unrelated to perceptual image compression. However, we find that theoretically: 1) Conditi…
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain
Zhe Wang, Xiaoliang Huo, Siqi Fan +3
In autonomous driving, The perception capabilities of the ego-vehicle can be improved with roadside sensors, which can provide a holistic view of the environment. However, existing…