Online agent using reinforcement learning to plan an open space trajectory for autonomous vehicles
US11467591B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 15, 2019 |
| Grant date | Oct 11, 2022 |
| Priority date | — |
| Expiry date | Sep 9, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In one embodiment, a system uses an actor-critic reinforcement learning model to generate a trajectory for an autonomous driving vehicle (ADV) in an open space. The system perceives an environment surrounding an ADV. The system applies a RL algorithm to an initial state of a planning trajectory based on the perceived environment to determine a plurality of controls for the ADV to advance to a plurality of trajectory states based on map and vehicle control information for the ADV. The system determines a reward prediction by the RL algorithm for each of the plurality of controls in view of a target destination state. The system generates a first trajectory from the trajectory states by maximizing the reward predictions to control the ADV autonomously according to the first trajectory.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.