Reference number : GC-2017-Q1-01608, amendment number : , agreement type : G, recipient legal name : Lashgarian Azad, Nasser (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Cooperative driving has received a great deal of attention in past years. Recent advances in vehicular communication systems enable the development of more intelligent driving assistance systems for a fleet of connected vehicles. Cooperative Adaptive Cruise Controller (CACC) is a driver assistance system for adjusting inter-vehicle distances through data exchanges between cars provided by wireless vehicle-to-vehicle (V2V) communications. In addition to vehicle safety and driver comfort, CACCs can improve significantly fuel economy by substantially reducing successive stop-and-go driving, especially in urban areas. In a string of vehicles supported by CACCs, the cars can accelerate or brake with less delays to minimize inter-vehicle distances, which leads to optimized traffic efficiency and enhanced road capacity. However, developing reliable CACCs for practical implementations, especially for urban driving, is a challenge. Sudden disturbances arising from a wide range of complex traffic scenarios, such as frequent stop-and-go driving sometimes with harsh decelerations and accelerations due to traffic lights, intersections, and emergency stops, as well as overtaking, lane-changing, cut-in and cut-out maneuvers, along with other sources of uncertainty like V2V communication defects and unexpected behavior of manually driven cars, can deteriorate the string stability of platoon and lead to car accidents. 
 
In this research, a novel synthesis framework for robust CACCs with optimal performance will be developed to maintain the string stability in the presence of various unknown disturbances and uncertainties in urban driving. Our plan is to develop and evaluate multiple robust CACCs by doing one of the core steps of our design methodology in several different ways. By the end of this program, we expect to identify an effective development methodology for robust CACCs with the best performance. The proposed CACCs will be designed for urban transport and use a broader range of data about upcoming driving (for instance, traffic lights, intersections, traffic jams, roadwork) obtained from on-board sensors to drive the vehicle safely with less fuel consumptions, while satisfying travel time and comfort criteria. These controllers will be developed for hybrid electric vehicles (HEVs), one of the near-term alternatives for sustainable transportations. HEVs have additional electric propulsion which makes them more complex than internal combustion engine vehicles. Our results will impact the area of cooperative driver assistance systems, and autonomous driving by some degree. The devised controllers will also add critical competitive value to the cars built by Canadian automotive companies and put them at the forefront of collaborative driving systems innovation. These CACCs will improve HEVs safety and fuel economy while enhancing road capacity and traffic efficiency., program purpose french : Cooperative driving has received a great deal of attention in past years. Recent advances in vehicular communication systems enable the development of more intelligent driving assistance systems for a fleet of connected vehicles. Cooperative Adaptive Cruise Controller (CACC) is a driver assistance system for adjusting inter-vehicle distances through data exchanges between cars provided by wireless vehicle-to-vehicle (V2V) communications. In addition to vehicle safety and driver comfort, CACCs can improve significantly fuel economy by substantially reducing successive stop-and-go driving, especially in urban areas. In a string of vehicles supported by CACCs, the cars can accelerate or brake with less delays to minimize inter-vehicle distances, which leads to optimized traffic efficiency and enhanced road capacity. However, developing reliable CACCs for practical implementations, especially for urban driving, is a challenge. Sudden disturbances arising from a wide range of complex traffic scenarios, such as frequent stop-and-go driving sometimes with harsh decelerations and accelerations due to traffic lights, intersections, and emergency stops, as well as overtaking, lane-changing, cut-in and cut-out maneuvers, along with other sources of uncertainty like V2V communication defects and unexpected behavior of manually driven cars, can deteriorate the string stability of platoon and lead to car accidents. 
 
In this research, a novel synthesis framework for robust CACCs with optimal performance will be developed to maintain the string stability in the presence of various unknown disturbances and uncertainties in urban driving. Our plan is to develop and evaluate multiple robust CACCs by doing one of the core steps of our design methodology in several different ways. By the end of this program, we expect to identify an effective development methodology for robust CACCs with the best performance. The proposed CACCs will be designed for urban transport and use a broader range of data about upcoming driving (for instance, traffic lights, intersections, traffic jams, roadwork) obtained from on-board sensors to drive the vehicle safely with less fuel consumptions, while satisfying travel time and comfort criteria. These controllers will be developed for hybrid electric vehicles (HEVs), one of the near-term alternatives for sustainable transportations. HEVs have additional electric propulsion which makes them more complex than internal combustion engine vehicles. Our results will impact the area of cooperative driver assistance systems, and autonomous driving by some degree. The devised controllers will also add critical competitive value to the cars built by Canadian automotive companies and put them at the forefront of collaborative driving systems innovation. These CACCs will improve HEVs safety and fuel economy while enhancing road capacity and traffic efficiency., agreement title english : Robust Cooperative Adaptive Cruise Control of Hybrid Electric Vehicles in Complex Urban Traffic Situations, agreement title french : Robust Cooperative Adaptive Cruise Control of Hybrid Electric Vehicles in Complex Urban Traffic Situations, agreement number : RGPIN, agreement value : 185000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada