DriveLM: Driving with Graph Visual Question Answering

1 code implementation • 21 Dec 2023 • Chonghao Sima, Katrin Renz, Kashyap Chitta, Li Chen, Hanxue Zhang, Chengen Xie, Jens Beißwenger, Ping Luo, Andreas Geiger, Hongyang Li

The experiments demonstrate that Graph VQA provides a simple, principled framework for reasoning about a driving scene, and DriveLM-Data provides a challenging benchmark for this task.

Autonomous Driving Question Answering +1

Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection

1 code implementation • NeurIPS 2023 • Linyan Huang, Zhiqi Li, Chonghao Sima, Wenhai Wang, Jingdong Wang, Yu Qiao, Hongyang Li

Current research is primarily dedicated to advancing the accuracy of camera-only 3D object detectors (apprentice) through the knowledge transferred from LiDAR- or multi-modal-based counterparts (expert).

3D Object Detection object-detection

Scene as Occupancy

2 code implementations • ICCV 2023 • Chonghao Sima, Wenwen Tong, Tai Wang, Li Chen, Silei Wu, Hanming Deng, Yi Gu, Lewei Lu, Ping Luo, Dahua Lin, Hongyang Li

Human driver can easily describe the complex traffic scene by visual system.

Decoder Motion Planning

OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping

1 code implementation • NeurIPS 2023 • Huijie Wang, Tianyu Li, Yang Li, Li Chen, Chonghao Sima, Zhenbo Liu, Bangjun Wang, Peijin Jia, Yuting Wang, Shengyin Jiang, Feng Wen, Hang Xu, Ping Luo, Junchi Yan, Wei zhang, Hongyang Li

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments.

3D Lane Detection

Sparse Dense Fusion for 3D Object Detection

no code implementations • 9 Apr 2023 • Yulu Gao, Chonghao Sima, Shaoshuai Shi, Shangzhe Di, Si Liu, Hongyang Li

With the prevalence of multimodal learning, camera-LiDAR fusion has gained popularity in 3D object detection.

3D Object Detection Object +1

Planning-oriented Autonomous Driving

1 code implementation • CVPR 2023 • Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, Hongyang Li

Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning.

Autonomous Driving Philosophy

Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe

2 code implementations • 12 Sep 2022 • Hongyang Li, Chonghao Sima, Jifeng Dai, Wenhai Wang, Lewei Lu, Huijie Wang, Jia Zeng, Zhiqi Li, Jiazhi Yang, Hanming Deng, Hao Tian, Enze Xie, Jiangwei Xie, Li Chen, Tianyu Li, Yang Li, Yulu Gao, Xiaosong Jia, Si Liu, Jianping Shi, Dahua Lin, Yu Qiao

As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.

Autonomous Driving

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

3 code implementations • 31 Mar 2022 • Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, Jifeng Dai

In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries.

3D Object Detection Autonomous Driving +2

PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark

2 code implementations • 21 Mar 2022 • Li Chen, Chonghao Sima, Yang Li, Zehan Zheng, Jiajie Xu, Xiangwei Geng, Hongyang Li, Conghui He, Jianping Shi, Yu Qiao, Junchi Yan

Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.).

3D Lane Detection Autonomous Driving +1

LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning

no code implementations • NeurIPS 2021 • Chonghao Sima, Yexiang Xue

The advancement of deep neural networks over the last decade has enabled progress in scientific knowledge discovery in the form of learning Partial Differential Equations (PDEs) directly from experiment data.