| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | September 19, 2016 |
| Latest Amendment Date: | April 20, 2020 |
| Award Number: | 1646556 |
| Award Instrument: | Standard Grant |
| Program Manager: |
David Corman
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2016 |
| End Date: | September 30, 2020 (Estimated) |
| Total Intended Award Amount: | $776,725.00 |
| Total Awarded Amount to Date: | $784,725.00 |
| Funds Obligated to Date: |
FY 2020 = $8,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3100 MARINE ST Boulder CO US 80309-0001 (303)492-6221 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3100 Marine Street, Room 481 Boulder CO US 80309-0572 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Special Projects - CNS, CPS-Cyber-Physical Systems |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The project studies techniques for constructing guaranteed-safe control algorithms for maneuvering autonomous vehicles ("self-driving cars") under a variety of environmental conditions. Existing autonomous vehicles are able to navigate highways and surface streets reliably when the driving conditions do not pose significant challenges. However, future vehicles will need to handle pot-holes, snow, high winds, driving rain, darting animals, fog and all the other impediments that make driving in the real world challenging in the first place. Some of these conditions require "aggressive maneuvers" in the form of sudden acceleration, braking and/or rapid steering. Such aggressive maneuvers present significant challenges to existing autonomy algorithms, raising concerns regarding the safety of the passengers, other vehicles on the road and pedestrians. At the same time, guaranteeing safe behavior while in autonomous operation is critical for the adoption of these systems, and such guarantees demand the development of reliable and verified maneuvering. The Ninja Car platform at the University of Colorado, Boulder serves as an experimental platform for the verified algorithms, and is also used to educate students and enthusiasts on the design and implementation of autonomous vehicles. The research carried out in this project contributes to the ultimate vision of self-driving cars that are safe by focusing on guaranteed-safe algorithms for maneuvering. Furthermore, the educational activities seek to educate a new generation of students and enthusiasts from the general public on the design and deployment of self-driving cars.
This project develops reliable control systems for maneuver regulation in autonomous ground vehicles that are adaptive to, and guaranteed for, a variety of driving conditions. The approach first considers the problem of developing a stack of increasingly complex models for autonomous vehicles. The simplest models serve to develop formally verified control algorithms for maneuver regulation and the corresponding set of maneuvers that can be carried out for varying road conditions. These results are transferred to more sophisticated models that use on-board sensors to fine-tune the control to the actual dynamics of the car (such as the wear on the shocks, tire pressure, etc.). Finally, building upon verified maneuvers for a single vehicle, the project studies cooperative maneuvers for multiple vehicles, wherein the vehicles communicate to meaningfully share information. The cooperating vehicles then implement verified collision avoidance schemes and share driving conditions (e.g. how slick a given road actually is) to formulate environment-aware, guaranteed-safe maneuvers.
The research extends the growing body of work on applying formal methods for rigorously solving control problems. A framework of transverse control Lyapunov and barrier functions provides a basis for solving trajectory tracking problems for nonlinear dynamical systems. The work also investigates new constraint-solving approaches for synthesizing these functions for nonlinear systems. The research is evaluated using a 1/8th-scale model testbed called the Ninja Car at the University of Colorado, Boulder. The research ideas are also integrated into educational activities that use the Ninja Car as a cost effective system for instructing engineering students at all levels, and enthusiasts interested in autonomous vehicles, on the fundamental principles that underlie the design and deployment of these systems.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project has focused on the development of safe, verifiable autonomous vehicle behavior for both an individual and a group of self-driving cars. We have developed techniques for individual cars to be able to maneuever in and between lanes while obeying certain constraints, for instance not turning too quickly or getting too close to other vehicles on the road. This approach was extended to multiple vehicles moving through typical road maneuvers, such as changing lanes. To do this, we employed techniques that reasoned over models of how other drivers might perceive the cars around them, and how their experience might reinforce certain behaviors that give rise to aggressive or passive driving. All of these approaches were also demonstrated on a small-scale experimental vehicle capable of driving and scaled high speeds in a lab environment. Furthermore, these results were shared with self-driving car companies and experts through invited visits and talks. Finally, a heavy emphasis of this work was its focus on educational objectives. Many PhD students were funded through this work, who have gone on to take positions in industry and academia in order to continue their effort on building safer self-driving cars. Undergraduate and Masters students have been exposed to these techniques through class projects and independent theses. The courses that were created out of the results of this research were packaged into exportable components and uploaded to GitHub, a site for archiving and sharing code and documentation for our techniques. A particular emphasis at the end of this award, due to the coronavirus pandemic, was the production of simulation tools for students to learn these techniques remotely: this is truly impactful as traditionally learning these experimental self-driving car techniques has required access to a physical testbed which was only available at well-funded research labs at universities.
Last Modified: 01/26/2021
Modified by: Sriram Sankaranarayanan
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