2024 International Autonomous Driving Challenge Officially Starts

• New competition topics: seven tracks, novel competition topics, comprehensive coverage of the latest and hottest topics in related fields, fully exploring the application of large models in the vertical fields of autonomous driving and embodied intelligence applications in.
• High prize money: The total prize pool exceeds 120,000 US dollars, and the maximum prize money for a single track can reach 27,000 US dollars. According to incomplete statistics, the reward amount is the highest among the 100 forums in CVPR 2024.
• Emphasis on originality: strengthen basic research and exploration, and adhere to the four aspects. Originality and scientific research value are used as the main measurement indicators, and rankings are not solely based on performance. Comprehensive ranking is carried out by combining original solutions and performance indicators.

On March 1, 2024, the 2024 International Autonomous Driving Challenge was officially launched. This challenge is hosted by Shanghai Artificial Intelligence Laboratory and co-organized by a number of domestic and foreign institutions. A number of well-known experts and scholars at home and abroad form the competition guidance and awards committee. This competition aims to provide an in-depth exploration of the tasks and challenges faced by autonomous systems and provide a stage for global participants to showcase technology and innovation. In this competition, we do not set too many restrictions on the participants. For example, multiple teams from the same organization are allowed to participate, all public data sets and pre-training weights are allowed to be used, and one team is allowed to win multiple awards at the same time. There are seven tracks in this competition, and the winner has the opportunity to receive a reward of up to 27,000 US dollars, and also has the opportunity to be invited to submit articles in top international journals. We have also prepared detailed competition guidelines and benchmark models for each track, which can be accessed by clicking on the corresponding link for each track.

Official website of the competition:

https://opendrivelab.com/challenge2024

Main activity:

CVPR 2024 Forum-Workshop on Foundation Models for Autonomous Systems (Seattle, USA)

Related events:

• Late April: The 3rd China 3D Vision Conference ( China3DV 2024)
• http://www.csig3dv.net/2024/competition.html
• Mid-June: Offline activities (Beijing/Shanghai)
  

Introduction to the competition topicEnd-to-end automatic driving

Due to the size of the previous data set Limited, inconsistent open- and closed-loop metrics make it difficult to benchmark sensorimotor-driven strategies using real data. In this track, large-scale data will be used to bridge the gap between the two evaluation paradigms, and through BEV abstract modeling in a short time series, efficient open-loop evaluation will be achieved while better aligning with closed-loop evaluation.

• Track information: https://www.php.cn/link/492f948808daf55a3f9e7ddd3694f546#end_to_end_driving_at_scale
• Data volume: about 2T
• Test server: https: //huggingface.co/spaces/AGC2024-P/e2e-driving-2024

World Model

As an abstract spatiotemporal representation of reality, The world model can predict the future state based on the observed current state, and learning the world model will promote the performance of the basic model to a new level. The model needs to predict the point cloud at future moments with only visual input to prove its ability to predict the world.

• Track information: https://www.php.cn/link/492f948808daf55a3f9e7ddd3694f546#predictive_world_model
• Data volume: about 2T
• Reference training time: sample Data, 8 cards A100, 3 days
• Test server: https://huggingface.co/spaces/AGC2024-P/predictive-world-model-2024

Occupation grid and motion estimation

Three-dimensional boxes are often insufficient to describe general objects. Inspired by the concept of robotics, perceptual representation can be described as the occupation of a gridded three-dimensional space. predict. In this track, contestants must not only provide a raster representation of the three-dimensional space, but also predict the movement of the raster.

• Track information: https://www.php.cn/link/492f948808daf55a3f9e7ddd3694f546#occupancy_and_flow
• Autonomous Driving Challenge @China3DV: https://huggingface.co/spaces /China3DV-S/occupancy-and-flow-2024
• Data volume: about 70GB
• Reference training time: 8 cards 3090, 2 days
• Test server: https: //huggingface.co/spaces/AGC2024-S/occupancy-and-flow-2024

Embodied multi-modal three-dimensional visual positioning

Compared with driving scenes, indoor embodied three-dimensional perception systems face multi-modal input including language instructions, more complex semantic understanding, more diverse object categories and orientations, and very different perception spaces. and needs. Based on this, the competition constructed a first-perspective multi-modal full-scene three-dimensional perception toolkit EmbodiedScan. The goal of this task is to detect the category of the target object and its oriented three-dimensional box given a verbal description of a specific object.

• Track information: https://www.php.cn/link/492f948808daf55a3f9e7ddd3694f546#multiview_3d_visual_grounding
• Data volume: about 1.5T
• Reference training time: Sample data 8 cards A100 0.5 days, full data 8 cards A100 3 days
• Test server: https://huggingface.co/spaces/AGC2024/visual-grounding-2024

CARLA Self-Driving Challenge

The CARLA Self-Driving Challenge requires vehicles to travel through a set of pre-defined routes. Vehicle driving routes involve complex situations, such as highways, urban areas, residential areas, and rural environments. They also include sunlight, sunset, night, rain, fog, and other light and weather conditions, which provide the possibility for closed-loop evaluation of autonomous driving systems.

• Track information: https://www.php.cn/link/492f948808daf55a3f9e7ddd3694f546#carla
• Test server: https://eval.ai/web/challenges/challenge -page/2098/overview

Application of large language model in autonomous driving

By introducing language information, The DriveLM data set connects large language models with autonomous driving systems, and ultimately makes decisions by introducing language reasoning capabilities to ensure the interpretability of planning. Taking multi-view images as input information, the model must answer various questions related to driving.

• Track information: https://www.php.cn/link/492f948808daf55a3f9e7ddd3694f546#driving_with_language
• Data volume: training set 4072 frames, test set 799 frames, each frame is about 90 question and answer pairs
• Reference training time: 8 cards V100, 1 day
• Test server: https://huggingface.co/spaces/AGC2024/driving-with-language-2024

Drive without a map

In the absence of high-definition maps, self-driving cars require a high level of scene understanding, and this track is designed to explore The limits of scenario reasoning capabilities. Taking multi-view images and standard-definition maps as input information, the neural network must not only output the perception results of lanes and traffic elements, but also output the topological relationships between lanes and between lanes and traffic elements.

• Track information: https://www.php.cn/link/492f948808daf55a3f9e7ddd3694f546#mapless_driving
• Autonomous Driving Challenge @China3DV: https://huggingface.co/spaces /China3DV/mapless-driving-2024
• Data volume: about 200G
• Reference training time: 8 cards V100, 1 day
• Test server: https://huggingface. co/spaces/AGC2024/mapless-driving-2024

Schedule

The following times are all Beijing time, please refer to the official website of the competition for details .

• From now on - June 1, 2024: Registration
• March 1, 2024: The competition officially begins
• March 25, 2024: Test server is open
• June 1, 2024: Test server is closed
• June 18, 2024: Competition results announced

Guidance and Awards Committee

Sorted by name strokes; the list is being updated continuously.

Qiao Yu	Shanghai Artificial Intelligence Laboratory	Leader Scientist, Assistant to the Director
Liu Qingshan	Nanjing University of Posts and Telecommunications	vice-president
##杨小康	Shanghai Jiao Tong University	Executive Dean of the Institute of Artificial Intelligence
Li Shengbo	Tsinghua University	Secretary of the Party Committee of the Vehicle College, national high-level leading talent, professor
Zhang Yaqin	Tsinghua University	Foreign academician of the Chinese Academy of Engineering, President of the Institute of Intelligent Industry, Chair Professor
Chen Baoquan	Peking University	Deputy Dean of the School of Intelligence, Boya Distinguished Professor
Xia Huaxia	Meituan	Chief Scientist, Vice President
##高新波	Chongqing University of Posts and Telecommunications	Deputy Secretary of the Party Committee, President, Professor
Xue Jianru	Xi'an Jiaotong University	Professor

Click "Read the original text" or visit the following link to learn more

https://opendrivelab.com/challenge2024

WeChat communication Group

About us-> Join the community

Registration link

##Contact email

workshop-e2e-ad@googlegroups.com

The above is the detailed content of 2024 International Autonomous Driving Challenge Officially Starts. For more information, please follow other related articles on the PHP Chinese website!