Optimizing Passenger Comfort in Cost Functions for Trajectory Planning
This work tackles passenger comfort as a key factor for adoption of self-driving cars, but it appears incremental as it builds on existing motion planning methods without claiming major breakthroughs.
The paper addresses the problem of incorporating passenger comfort into cost functions for autonomous vehicle trajectory planning, proposing a formulation based on human perception of comfort.
Current advances in the development of autonomous cars suggest that driverless cars may see wide-scale deployment in the near future. Research by both industry and academia is driven by potential benefits of this new technology, including reductions in fatalities and improvements in traffic and fuel efficiency as well as greater mobility for people who will or cannot drive cars themselves. A deciding factor for the adoption of self-driving cars besides safety will be the comfort of the passengers. This report looks at cost functions currently used in motion planning methods for autonomous on-road driving. Specifically, how the human perception of how comfortable a trajectory is can be formulated within cost functions.