Integrated motion planning and control for automated vehicles up to the limits of handling
Abstract/Contents
- Abstract
- In order to keep self-driving cars safe, it is important that these vehicles can plan safe trajectories through their environment and have the ability to robustly use their full tire-force potential. Racing at the limits of handling provides a purposefully challenging scenario for the development of reliable vehicle-motion planning and control techniques, as race cars are constantly pushed to their physical limits. With a common trajectory-tracking architecture for automated vehicle control, steering provides path-tracking control, and the throttle and brakes are used to track a desired speed profile. For the specific application of racing, this speed profile can be designed to fully use the tire-force potential. Experimental data show that a preexisting control framework based on this approach can match the lap time of an amateur race-car driver, but a professional race-car driver proves to be slightly faster. It is demonstrated with both experimental results and an analytical method that with this decoupled path-tracking and speed-tracking controller, an automated vehicle is prone to either under-utilize the tires or lose control over the path-tracking dynamics when unintentionally operating beyond the limit. Furthermore, a professional race-car driver successfully operates the vehicle with a control strategy that seems fundamentally different from trajectory tracking. Namely, he shows significant lap-to-lap variations in both speed and path, but he is consistently faster than automated trajectory-tracking control. This inspires new strategies for automated vehicle control. In this context, two novel feedback-control strategies are presented, which employ slip-angle control to robustly use the vehicle's full tire-force potential, while speed control provides the path-tracking functionality. Subsequently, in order to have the ability to also adjust the vehicle's path, a Nonlinear Model Predictive Control (NMPC) framework is presented which can trade-off longitudinal and lateral control inputs. Experimental results demonstrate that this controller successfully coordinates the inputs at the limits of handling. However, the computational burden of this NMPC framework limits the length of the planning horizon for real-time control, which in turn inhibits its ability to adjust the vehicle's path and speed. To address this issue, a new NMPC framework is developed, which serially cascades vehicle models of different levels of complexity in the planning horizon. Experimental results on an automated race car demonstrate the benefits of this new concept, with a high quality of control provided by a high-fidelity vehicle model in the near-term planning horizon, and significant extension of the planning horizon with a low-fidelity model.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Laurense, Vincent Andreas |
|---|---|
| Degree supervisor | Gerdes, J. Christian |
| Thesis advisor | Gerdes, J. Christian |
| Thesis advisor | Pavone, Marco, 1980- |
| Thesis advisor | Schwager, Mac |
| Degree committee member | Pavone, Marco, 1980- |
| Degree committee member | Schwager, Mac |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Vincent A. Laurense. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Vincent Andreas Laurense
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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