Teaching Resources Library

🌟 Overview

"Teaching Resource Library" It is a special column specially created for college teachers and transportation students majoring in the field of autonomous driving. Here, college teachers can obtain teaching PPT and other materials on example models for the five major issues of perception, prediction, decision planning, control, and cutting-edge issues to assist in daily teaching; students majoring in transportation, automobiles, control, etc. can use this to deeply understand the principles of the example models and strengthen their professional knowledge.

The OnSite teaching resource library focuses on the "perception-prediction-decision-control-frontier" technology closed loop of autonomous driving system development and provides modular teaching resources.

Teaching Resources Preview

  • Zero-Preparation Switching:The PPT teaching resources have a unified format and clear content structure, and support quick switching from online to offline explanations. Teachers can directly use them in different teaching scenarios without additional preparation or modification, achieving zero-cost migration.

  • Ready to Use:All PPTs are equipped with sample codes and experimental guidance, focusing on key and difficult points. Beginners can build a complete knowledge framework in a short time, quickly get started with key concepts, and meet the various needs of teaching and self-study.

  • Ask GPT Anything:Each teaching resource can be used with OnSite's built-in large model assistant (such as OnsiteGPT) to realize interactive Q&A. Students can ask questions at any time based on the slide content, gain in-depth understanding, build a personalized learning path, and improve learning initiative.

Lecture 1

Perception: Fundamentals

Lecture 2

Perception Problem: Depth Estimation

Lecture 3

Perception problem: semantic segmentation

Lecture 4

Perception problem: 2D object detection

Lecture 5

Prediction: Single-Vehicle, Single-Modal

Lecture 6

Prediction problem: multi-vehicle single mode

Lecture 7

Prediction problem: VRU trajectory prediction

Lecture 8

Decision-Making and Planning: Single-Vehicle Behavior Decision-Making

Lecture 9

Decision Planning Problems: Collaborative Decision-Making

Lecture 10

Decision planning problem: trajectory planning

Lecture 11

Control: Autonomous-Driving Motion Control Overview

Lecture 12

Control issues: typical methods and cases of vertical control

Lecture 13

Control issues: Typical methods and cases of lateral control

Lecture 14

Control issues: Typical methods and cases of coupling control

Lecture 15

Control Issues: Data-Driven Control

Lecture 16

Autonomous-Driving Frontiers: End-to-End Approaches

Lecture 17

Autonomous Driving Frontier: Autonomous Driving Supported by LLM

Lecture 18

Autonomous Driving Frontier: VLM & VLA

Lecture 19

Frontiers of Autonomous Driving: A World Model

If you want to access the rich teaching resource library of OnSite Learning Center, or inquire about resource usage, course content docking, personalized resource recommendations, etc., please feel free to contact us through the following channels. Our teaching support team will provide you with detailed guidance and help answer all questions about resource acquisition, permission activation and special teaching projects to help you carry out teaching activities efficiently.

Resource acquisition form link:https://forms.gle/BML8RHRuwREXdZpa7

🚦 Module Development Progress

👁 Perception Module

– [√] Perception basis and target recognition
– [√] Semantic Segmentation
– [√] Depth Estimation
– [√] 2DObject Detection

🔮 Prediction Module
– [√] Introduction to prediction and bicycle trajectory prediction
– [√] Multi-vehicle trajectory prediction
– [√] VRU trajectory prediction

 🧠 Decision-Making and Planning Module
– [√] Bike Decisions
– [√] collaborative decision-making
– [√] Motion planning

🎯 Control Module
– [√] Control Basics
– [√] Longitudinal control PID
– [√] Lateral control MPC
– [√] Coupling control LQR
– [√] Data-driven control

📃 Test
– [√] Test theory
– [√] Test method
– [√] Test practice

💻 Frontier Technologies
– [√] End-to-end autonomous driving
– [√] LLM supported decision-making
– [√] world model
– [√] VLM and VLA