Autonomous Planning and Control for Intelligent Vehicles in Traffic
| Autor: | Panagiotis Tsiotras, Changxi You, Dimitar Petrov Filev, Jianbo Lu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Truck
050210 logistics & transportation Computer science Mechanical Engineering 05 social sciences Decision tree Markov process Control engineering Computer Science Applications Vehicle dynamics symbols.namesake Overtaking 0502 economics and business Automotive Engineering symbols Reinforcement learning Motion planning Markov decision process |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 21:2339-2349 |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2019.2918071 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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