Motorcycle safety has been improving in recent years with better designed protective gear and the integration of certain vehicle technologies like anti-lock braking systems into motorcycles. But there is still a sizable gap in the safety features found in contemporary cars and those found in motorcycles. The vision of this project is to capitalize on the use of blind spot detection for motorcycles. Like cars, the system will monitor for objects in the rider’s blind spot and give them a soft alert if an obstacle is detected.
The future of automotive technology, safety, and autonomy relies heavily in connected vehicles using Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication. Almost all vehicles on the road today are not capable of V2V and V2I communication, putting them at a serious disadvantage in the future. The proposed solution for this project is to design and integrate an add-on solution that can be equipped to vehicles to give them V2V and V2I capability; keeping today’s vehicles relevant tomorrow.
Optimizing Control Strategies for Hybrid Electric Vehicles to Reduce Fuel Consumption and Idling Times
More contemporary vehicles are being sold as hybrid-electric models that use the combination of a combustion engine and an electric motor. Additionally, future vehicles are expected to be integrated with Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication technology. The goal of this project is to take advantage of this new technology and design algorithms that manage the torque split and driving routes of hybrid-electric vehicles to improve their fuel efficiency and reduce idling times. Specifically, this research will focus on optimizing and reducing congestion seen in fast-food outlet stores. Future work consists of applying these methods to other areas of traffic as well.