1 research outputs found
Task-Motion Planning for Safe and Efficient Urban Driving
Autonomous vehicles need to plan at the task level to compute a sequence of
symbolic actions, such as merging left and turning right, to fulfill people's
service requests, where efficiency is the main concern. At the same time, the
vehicles must compute continuous trajectories to perform actions at the motion
level, where safety is the most important. Task-motion planning in autonomous
driving faces the problem of maximizing task-level efficiency while ensuring
motion-level safety. To this end, we develop algorithm Task-Motion Planning for
Urban Driving (TMPUD) that, for the first time, enables the task and motion
planners to communicate about the safety level of driving behaviors. TMPUD has
been evaluated using a realistic urban driving simulation platform. Results
suggest that TMPUD performs significantly better than competitive baselines
from the literature in efficiency, while ensuring the safety of driving
behaviors