Intelligent and Automatic Motion Planning for Self-Driving Vehicles
Project Abstract/Statement of Work:
Motion planning is a fundamental task for self-driving cars.
Connected automated vehicles (CAVs) deployed in real-world traffic can be surrounded with many moving/stationary obstacles. In many cases, the pre-defined path was obtained by having the vehicle driven manually once or more than one time—rendering this approach not scalable for deployment in the real world.
This proposed work aims to develop, implement and test algorithms for intelligent and automatic motion planning, for both RTK-centric and Sensor-centric self-driving cars. Being “intelligent” means that the path will be generated by considering a wide range of factors, road/lane constraints, dynamic interactions with other road users, traffic rules, vehicle dynamics, and environmental constraints. Being “automatic” means that human inputs are only required at the initiation of the process or during algorithm calibration, and not afterwards. We will start from traditional deterministic model/theory based approaches or AI based algorithms such as reinforcement learning, and will extend to consider the stochastic nature of dynamic self-driving.
PI and Co-PI:
- Huei Peng
- Shaobing Xu