DiDi Autonomous Driving is developing Level 4 autonomous driving (AD) technology to make transportation safer and more efficient by integrating AD technology into a shared-mobility fleet.
Requirements
- Strong proficiency in C++ and Python for implementing complex, real-time algorithms.
- Solid understanding of robotics fundamentals, including decision-making, motion planning, control theory, trajectory ranking, search and optimization algorithms etc.
- Related experience in one or more of the following: behavioral planning, motion planning, behavior and world environment reasoning, trajectory ranking and cost design.
- Knowledge of vehicle dynamics and longitudinal/lateral control systems.
- Solid understanding of machine learning principles, reinforcement learning and related algorithms.
Responsibilities
- Design and implement the core Behavioral Planning logic that determines the vehicle's high-level actions (e.g., lane changes, merges, yields, and interactions with other agents).
- Develop and optimize the motion planning algorithms that execute behavioral decisions, integrating Geometry Reasoning (path) and Speed Reasoning (velocity) into a cohesive trajectory.
- Architect and enhance the geometry system for generating geometrically feasible and compliant paths.
- Architect and refine the velocity system for generating context-aware, comfortable, and safe velocity profiles.
- Model complex driving scenarios and agent interactions to create a robust world model for the behavioral planner.
- Design different costs for trajectory ranking to trade off ETAs, comfort and safety of the vehicle behaviors.
- Conduct in-depth analysis, testing, and debugging of the system's performance in various scenarios, leading root cause investigations.
Other
- Experience in autonomous systems, robotics, or automotive software development.
- PhD or internship experience related to robotics planning system designs.
