Grant: Research Tools and Instruments
University of Waterloo
$150,000.00
Waterloo Autonomous Golf Cart Testbed
EF 2020-21
Résumé
Automated vehicles (AVs) improve safety, mobility, accessibility, energy efficiency and create significant economic growth opportunities. Advances in control and motion-planning are crucial to AV deployment, but major challenges continue to arise, particularly related to the complexities of using AVs as personal transporters in unpredictable, pedestrian-rich environments (e.g., amusement parks and airport terminals) and in complex, multi-vehicle, and highly dynamic situations (e.g., roundabouts and intersections) where lateral and longitudinal collisions are more likely.
Our team (Lashgarian Azad, Jeon, Dautenhahn, Fischmeister) is conducting cutting-edge research on safe, socially-compliant, and reliable AV control & motion planning for both pedestrian-rich and road environments. We are requesting a full-scale AV testbed, specifically, an autonomous golf cart (AGC), to test our new AV algorithms. The requested AGC will be used to conduct the required experimental work and data collections for validating and optimizing our methodologies for the following research topics: (i) traffic participant behavior predictions, (ii) socially-compliant motion planning in crowded environments, and (iii) safe decision-making and controls at roundabouts and intersections. We have developed AV controllers for roundabout driving that successfully integrate model predictive control and reinforcement learning to achieve safe motions in complex multi-vehicle traffic scenarios. We are also designing novel data-driven AV controllers with safety assurance using the concept of control barrier functions. Our early-stage research takes full advantage of virtual simulations and scaled car experiments, but we require a vehicle for full-scale experiments to validate our novel AV controls and decision-making techniques for practical situations. The requested AGC instrumented with on-board sensors and a programmable controller is immediately needed to serve as our full-scale testbed. The programmable controller allows quick deployments of revised versions of our AV algorithms and optimizing them for real-life situations.
The requested testbed will enable unique opportunities for technological breakthroughs towards the safe and efficient use of AVs in pedestrian zones and complex, on-road scenarios. There are strong and growing demands for highly skilled people in the AV sector. The research enabled by the AGC will provide a unique training environment for the next generation of AV innovators, equipping them with hands-on experience in adopting motion planning and control technologies while advancing their knowledge in learning-based methods, human-machine interactions, and safety assurance techniques. In addition to driverless cars, self-driving delivery & on-demand mobility services, and autonomous shuttles, our research results will impact other industries like construction, mining, farming, and forestry, creating great opportunities to achieve major economic benefits.
Programme
Research Tools and Instruments
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
RTI-2021-00103
Name nom
Lashgarian Azad, Nasser
Department département
Systems Design Engineering
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2021
Award amount
150000
Program id
RTI
Program name en
Research Tools and Instruments
Program name fr
Outils et instruments de recherche
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1612
Committee name en
Mechanical Engineering (RTI)
Committee name fr
Génie mecanique (OIR)
Area of application code
700
Area of application group en
Transportation systems and services
Area of application group fr
Services et systèmes de transport
Area of application en
Transportation systems and services
Area of application fr
Services et systèmes de transport
Research subject code
2108
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Aerospace, aeronautical and automotive engineering
Research subject fr
Génie aérospatial, aéronautique et automobile
Installment
inst:1-1
Partie
1
Nb partie
1
Application title
Waterloo Autonomous Golf Cart Testbed
Keyword
NSERC
Application summary
Automated vehicles (AVs) improve safety, mobility, accessibility, energy efficiency and create significant economic growth opportunities. Advances in control and motion-planning are crucial to AV deployment, but major challenges continue to arise, particularly related to the complexities of using AVs as personal transporters in unpredictable, pedestrian-rich environments (e.g., amusement parks and airport terminals) and in complex, multi-vehicle, and highly dynamic situations (e.g., roundabouts and intersections) where lateral and longitudinal collisions are more likely.
Our team (Lashgarian Azad, Jeon, Dautenhahn, Fischmeister) is conducting cutting-edge research on safe, socially-compliant, and reliable AV control & motion planning for both pedestrian-rich and road environments. We are requesting a full-scale AV testbed, specifically, an autonomous golf cart (AGC), to test our new AV algorithms. The requested AGC will be used to conduct the required experimental work and data collections for validating and optimizing our methodologies for the following research topics: (i) traffic participant behavior predictions, (ii) socially-compliant motion planning in crowded environments, and (iii) safe decision-making and controls at roundabouts and intersections. We have developed AV controllers for roundabout driving that successfully integrate model predictive control and reinforcement learning to achieve safe motions in complex multi-vehicle traffic scenarios. We are also designing novel data-driven AV controllers with safety assurance using the concept of control barrier functions. Our early-stage research takes full advantage of virtual simulations and scaled car experiments, but we require a vehicle for full-scale experiments to validate our novel AV controls and decision-making techniques for practical situations. The requested AGC instrumented with on-board sensors and a programmable controller is immediately needed to serve as our full-scale testbed. The programmable controller allows quick deployments of revised versions of our AV algorithms and optimizing them for real-life situations.
The requested testbed will enable unique opportunities for technological breakthroughs towards the safe and efficient use of AVs in pedestrian zones and complex, on-road scenarios. There are strong and growing demands for highly skilled people in the AV sector. The research enabled by the AGC will provide a unique training environment for the next generation of AV innovators, equipping them with hands-on experience in adopting motion planning and control technologies while advancing their knowledge in learning-based methods, human-machine interactions, and safety assurance techniques. In addition to driverless cars, self-driving delivery & on-demand mobility services, and autonomous shuttles, our research results will impact other industries like construction, mining, farming, and forestry, creating great opportunities to achieve major economic benefits.