Qichao Zhao

Cheng Han, Qichao Zhao, Shuyi Zhang et al.

Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance. It has become a popular paradigm when designing networks for real-time practical autonomous driving system, where computation resources are limited. This paper proposed an effective and efficient multi-task learning network to simultaneously perform the task of traffic object detection, drivable road area segmentation and lane detection. Our model achieved the new state-of-the-art (SOTA) performance in terms of accuracy and speed on the challenging BDD100K dataset. Especially, the inference time is reduced by half compared to the previous SOTA model. Code will be released in the near future.

Zihan You, Ni Wang, Hao Wang et al.

Accurate 3D object detection in autonomous driving relies on Bird's Eye View (BEV) perception and effective temporal fusion.However, existing fusion strategies based on convolutional layers or deformable self attention struggle with global context modeling in BEV space,leading to lower accuracy for large objects. To address this, we introduce MambaBEV, a novel BEV based 3D object detection model that leverages Mamba2, an advanced state space model (SSM) optimized for long sequence processing.Our key contribution is TemporalMamba, a temporal fusion module that enhances global awareness by introducing a BEV feature discrete rearrangement mechanism tailored for Mamba's sequential processing. Additionally, we propose Mamba based DETR as the detection head to improve multi object representation.Evaluations on the nuScenes dataset demonstrate that MambaBEV base achieves an NDS of 51.7\% and an mAP of 42.7\%.Furthermore, an end to end autonomous driving paradigm validates its effectiveness in motion forecasting and planning.Our results highlight the potential of SSMs in autonomous driving perception, particularly in enhancing global context understanding and large object detection.