Abstract
This project focuses on developing a training community for engineering and ethical skills for developing future autonomous vehicles. This project includes three components - (1) autonomous driving course development with multiple scale vehicles from 1/18th-scale, 1/10th-scale, 1/5th-scale to full scale vehicles where students learn advanced algorithms and software development for perception, planning and control of autonomous driving; (2) Community Activities spanning 89 universities which have one or more RoboRacer platforms and participate in the international autonomous racing competitions. We will host a minimum of 4 competitions in the top robotics, transportation and cyber-physical systems conferences; (3) Development of an ethical framework for using machine learning in life-critical systems.
Contribution: An autonomous vehicle hardware and software platform, called RoboRacer, is developed for teaching autonomous systems hands-on. This project will design and develop the education modules and software stack for teaching at various educational levels with the theme of ``racing" and competitions that replace exams. We have expanded the previous F1Tenth effort to now include multiple scale vehicles that use the same autonomous driving software. The important aspect here is that it allows students to mature through more complex driving platforms, sensor suites, algorithm pipelines and safety scenarios - all the way to full scale autonomous vehicles.
Background: College-level robotics courses often focus on theory, while most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. The RoboRacer platforms fill the gap between research platforms and low-end toy cars and offers the hands-on experience in learning the topics in autonomous systems.
Intended Outcomes: The RoboRacer vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students' computational thinking through autonomous driving with the RoboRacer vehicle.
Application Design: Over 89 universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the RoboRacer platform. This project's focus is to maintain and grow this community through education, outreach and K-12 training events.
Findings: More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course.
Description
Timeline
Strategic Description / RD&T
Section left blank until USDOT’s new priorities and RD&T strategic goals are available in Spring 2026.
Deployment Plan
Q1: We will develop https://courses.roboracer.ai for online offerings of this curriculum.
The course will be taught again in January-May 2025 and 2026. More details on the overall project is at https://roboracer.ai
Q2: We have hosted 24 international autonomous racing competitions - https://roboracer.ai/race
The last competition was attended by over 180 participants from 37 teams. All teams built the same reference platform and competed based on their autonomous racing algorithms. The next races will by at TRB'25, ICRA'25 Atlanta (May 2025), IEEE Intelligent Vehicles'25 (June 2025), CPSWeek'25 (May 2025) and 4 other top conference venues for transportation, control systems, robotics and cyber-physical systems.
Q3: RoboRacer will integrate the Autoware open-source autonomous driving software stack to the same software will be available on 1/18, 1/10, 1/5 and full-scale vehicle platforms. This will be demonstrated in side-by-side deployments using a variety os sensors such as lidars, cameras, GNSS, IMUs, etc. We will demonstrate this in closed-circuit deployments at the Safety21 deployment partners summit.
Q4: All materials (course lectures, videos, code, simulators, data sheets, and schematics) will be documented, packaged and shared as free and open-source with the public.
Expected Outcomes/Impacts
The course instructors will develop 10 reference platform vehicles and demonstrate them working at high speeds of 8-35mph
We will host 5 international competitions with over 80 academic partners worldwide. These will be hosted at top conference venues for transportation, control systems, robotics and cyber-physical systems.
It will provide training in autonomous vehicle development to over 2000 persons worldwide and extend the state of the art for machine perception, motion planning and safe adaptive control research.
This effort will raise the awareness of ethical issues with using machine learning in future mobility systems.
Expected Outputs
Theme I: Safe Autonomy - This thrust will enable AV controllers that combine the performance and generalization abilities of machine learning with the safety guarantees afforded by formal and semi-formal verification. Researchers in this theme develop fast verification methods that scale to run in real-time on-board the vehicle through a combination of formal methods and testing. A cloud-based simulator will enable scalable verification which combines robust testing and falsification with reachability analysis for real systems.
Theme II: Efficient Autonomy - Researchers in this theme develop the hardware and software architectures for power-efficient and timing-guaranteed execution of autonomy algorithms. These include computer vision, motion planning, and neural network inference engines.
Theme III: Coordinated Autonomy - This thrust will enable a fleet of AVs to coordinate on-the-fly to achieve fleet-wide safety, higher transportation network efficiencies and enable exploration of new mobility and ridesharing services.
Theme IV: Secure Autonomy - Researchers in this thrust develop models of cyber-physical attacks, and resilient estimation and control schemes to guard against them and mitigate their effects in the field.
TRID
The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods and approaches that are used in the fields of perception, planning and control as well as end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to a range of high performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms emphasizing both the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field we conclude with a summary of open research challenges that will guide future researchers in this field.
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| rahulm@seas.upenn.edu |
Mangharam, Rahul |
University of Pennsylvania |
PI |
Faculty - Tenured |
| lee@cis.upenn.edu |
Mangharam, Rahul |
University of Pennsylvania |
Co-PI |
Faculty - Tenured |
Budget
Amount of UTC Funds Awarded
$120000.00
Total Project Budget (from all funding sources)
$220000.00
Documents
Match Sources
No match sources!
Partners
| Name |
Type |
| The Autoware Foundation |
Deployment Partner Deployment Partner |
| Robotics Automation Society |
None |
