Develop and optimize spatio-temporal joint planning algorithms for highways and urban roads to improve the success rate and human-like behavior of lane-changing maneuvers while reducing unnecessary lane changes.
Optimize models for intersections and non-intersection roads in memory driving and urban NOA (Navigation on Autopilot), including fusion of visual perception and online mapping.
Adapt frameworks for memory driving and end-to-end projects to efficiently support multi-project codebase compatibility.
Job requirements
Experience in mass production of memory driving and urban NOA systems, or deep involvement in decision-making and planning development for related projects. Familiarity with leading spatio-temporal joint algorithms in the industry.
Proficient in C++ and traditional data structure algorithms, with the ability to efficiently implement new features.
Strong communication skills, sense of responsibility, and teamwork spirit.