Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 21, Issue 6, Pages 2339-2349Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2918071
Keywords
TV; Planning; Path planning; Roads; Vehicle dynamics; Autonomous vehicles; Reinforcement learning; Bezier curve; curvature constraint; dynamic cell; path planning; autonomous vehicle
Categories
Funding
- National Science Foundation [CPS-1544814]
- Ford Motor Company
Ask authors/readers for more resources
This paper addresses the trajectory planning problem for autonomous vehicles in traffic. We build a stochastic Markov decision process (MDP) model to represent the behaviors of the vehicles. This MDP model takes into account the road geometry and is able to reproduce more diverse driving styles. We introduce a new concept, namely, the dynamic cell, to dynamically modify the state of the traffic according to different vehicle velocities, driver intents (signals), and the sizes of the surrounding vehicles (i.e., truck, sedan, and so on). We then use Bezier curves to plan smooth paths for lane switching. The maximum curvature of the path is enforced via certain design parameters. By designing suitable reward functions, different desired driving styles of the intelligent vehicle can be achieved by solving a reinforcement learning problem. The desired driving behaviors (i.e., autonomous highway overtaking) are demonstrated with an in-house developed traffic simulator.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available