OnSite Learning Center
An Autonomous Driving Dataset Learning and Application Platform
The OnSite Learning Center is a comprehensive platform for learning, communication, and resource sharing in autonomous driving. It organizes learning resources such as datasets, sample models, benchmark models, and leaderboards by research problem and task, helping learners build a systematic understanding of autonomous driving technology. The platform provides categorized services for users at different levels, helping beginners get started quickly, supporting advanced learners in deeper research, encouraging algorithm developers to contribute and co-build, and fostering a complete autonomous driving learning community.
The OnSite Learning Center organizes learning resources around the chain of "problem - task - dataset - sample model - benchmark model - leaderboard." The "problem - task - dataset - sample model" path provides beginners with foundational theory and hands-on implementation of basic models. Benchmark models, contributed by algorithm developers, provide advanced model resources for professional learners. Leaderboards are designed for algorithm developers and help drive the iterative improvement of autonomous driving algorithms. By offering tiered and personalized services, the platform helps beginners learn autonomous driving technology efficiently and supports both autonomous driving education and algorithm development.
Understand the technical system and try sample models
- The OnSite Learning Center providesBeginning Learnerswith curated learning resources, including introductions to core concepts and hands-on sample code.
- InQuick Start, you can access these resources. For the five problem areas of perception, prediction, decision and planning, control, and end-to-end learning, we provide problem and task descriptions as well as step-by-step tutorials for mainstream foundational research models. Learners can deepen their understanding of theory through hands-on practice after mastering the basics.
Online Code Practice Guide
To lower the barrier to learning, the OnSite Learning Center provides acloud-based code practice environment, built on JetBrains Datalore and supporting interactive Notebook learning in Python. You can read learning documents online and run code directly without configuring a local environment:
- Online workflow:
- Click the "Run Code Online" button in a course to open the Datalore shared project page
- Sign in or register with a Google email or JetBrains account (Register here)
- Click "Copy to Private Workspace" to copy the project into your personal workspace
- Run code blocks one by one in your private Notebook or execute the full workflow with one click
- Tips:
- Shared projects are view-only; copy them to a private workspace before editing
- Notebooks combine illustrated explanations with code modules and support interactive debugging
- Local execution requires configuring a Conda environment in PyCharm; see the project guide for details
Learn benchmark models and use the resource library
- The OnSite Learning Center also providesAdvanced Learnerswith rich learning resources. After mastering the fundamentals, users can visitBenchmark Modelsfor more advanced algorithm models, or use the Resource Library to learn more aboutDatasetsandutility functions.
- Benchmark Models provide learners with a structured and standardized resource library. They bring together the latest research results and allow learners to contribute their own models through collaborative development. For each task, the OnSite Learning Center defines a systematic workflow so learners can get started efficiently.
- The Resource Library collects datasets, utility functions, development templates, and other resources, helping learners broaden their perspective and deepen their understanding of autonomous driving tasks.
Develop benchmark models and join the leaderboards
- The OnSite Learning Center encourages everyAlgorithm Developersto contribute to OnSite through collaborative development and sharing, including joining leaderboards for research problems and tasks and participating in community discussions.
- By visitingLeaderboards, experts can clearly understand the current state of models for each research task. Models on the leaderboards may be included as benchmark models for other learners.
- InRecommended Templatespages provide a file structure for each problem and task, helping beginning learners compare models and transfer methods within the same task.
Join the community and co-build
- We encourage all users to contribute by sharing problems, tasks, datasets, or models discovered through research and practice with the OnSite Learning Center. Whether you have found a new dataset, implemented a state-of-the-art model, or encountered a technical challenge worth discussing, you can submit your contribution through the platform. The Learning Center encourages collaborative development and sharing, including participation in leaderboards and community discussions for different research problems and tasks.
