Offline agent using reinforcement learning to speedup trajectory planning for autonomous vehicles
US11493926B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 15, 2019 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | Nov 15, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In one embodiment, a system generates a plurality of driving scenarios to train a reinforcement learning (RL) agent and replays each of the driving scenarios to train the RL agent by: applying a RL algorithm to an initial state of a driving scenario to determine a number of control actions from a number of discretized control/action options for the ADV to advance to a number of trajectory states which are based on a number of discretized trajectory state options, determining a reward prediction by the RL algorithm for each of the controls/actions, determining a judgment score for the trajectory states, and updating the RL agent based on the judgment score.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.