Automatic parameter tuning framework for controllers used in autonomous driving vehicles
US11731651B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 30, 2020 |
| Grant date | Aug 22, 2023 |
| Priority date | — |
| Expiry date | Apr 2, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01C21/26
- WIPO fieldTransport
- WIPO sectorMechanical engineering
Abstract
Systems and methods are disclosed for optimizing values of a set of tunable parameters of an autonomous driving vehicle (ADV). The controllers can be a linear quadratic regular, a “bicycle model,” a model-reference adaptive controller (MRAC) that reduces actuation latency in control subsystems such as steering, braking, and throttle, or other controller (“controllers”). An optimizer selects a set tunable parameters for the controllers. A task distribution system pairs each set of parameters with each of a plurality of simulated driving scenarios, and dispatches a task to the simulator to perform the simulation with the set of parameters. Each simulation is scored. A weighted score is generated from the simulation. The optimizer uses the weighted score as a target objective for a next iteration of the optimizer, for a fixed number of iterations. A physical real-world ADV is navigated using the optimized set of parameters for the controllers in the ADV.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.