Model fidelity and trajectory planning for autonomous vehicles at the limit
Abstract/Contents
- Abstract
- Autonomous vehicles have the potential to greatly improve transportation safety by eliminating many automobile accidents, the vast majority of which are caused by human error. However, for cars to be able to avoid an accident whenever physically possible, they will have to drive at least as well as the best human drivers. Racing drivers can claim to be the best drivers in the world since, by the nature of their sport, they are forced to consistently and safely operate the vehicle at its physical limits. Autonomous racing provides an avenue to rapidly develop insights and control strategies for autonomous vehicles that are applicable to emergencies on public roads. This thesis expands the understanding of what effects must be captured for a vehicle to drive at the limits of friction. First, the impact of road topography on the vehicle's limits is discussed and modeled. Experiments with an automated vehicle show that accounting for topography-driven variation in normal load is critical for ensuring that the vehicle stays within its limits. The same simple model used to generate those insights is also useful for rapid trajectory replanning, illustrated here through examples covering obstacle avoidance and racing line optimization. This ap- proach to trajectory modification constitutes the second contribution of this thesis. While the simple model upon which the method is based captures the most funda- mental limitations of the vehicle, it is worth examining the extent to which more complex models of the vehicle's dynamics lead to better performance. An evaluation of the utility of several possible models for generating trajectories at the limit on various surfaces, including ice, wet asphalt, and dry asphalt, shows that the models' prescriptions for the optimal trajectory vary little and that all can be used success- fully. However, a significant advantage of the more complex models is that the many actuators available on modern vehicles may be used in a coordinated fashion to better accomplish the desired control objective. To this end, a novel model of the effects of a limited slip differential is incorporated into the double-track model of the vehi- cle. The insights from this work can be used to design algorithms that operate over the full range of vehicle performance, maximizing an autonomous vehicle's ability to operate skillfully when racing or safely when confronted with an emergency
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Subosits, John Karl |
|---|---|
| Degree supervisor | Gerdes, J. Christian |
| Thesis advisor | Gerdes, J. Christian |
| Thesis advisor | Cutkosky, Mark R |
| Thesis advisor | Pavone, Marco, 1980- |
| Degree committee member | Cutkosky, Mark R |
| Degree committee member | Pavone, Marco, 1980- |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | John K. Subosits |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by John Karl Subosits
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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