AI-Driven Intersection Safety System for Vulnerable Road User Protection
TS-070954 — The Need
Intersections are among the most dangerous areas on U.S. roadways, accounting for approximately 25% of traffic fatalities and nearly half of all injuries annually. Vulnerable Road Users (VRUs), including pedestrians and cyclists, face increasing risk due to complex traffic dynamics and limi…
- College: College of Engineering (COE)
- Inventors: Yurtsever, Ekim; Giuliani, Michele; Rizzoni, Giorgio
- Licensing Officer: Ashouripashaki, Mandana
Predictive V2X Torque Control for Multi-E-Axle EV Efficiency
TS-070632 — The Need
As electric vehicles (EVs) evolve toward multi-e-axle architectures, traditional energy management strategies fall short in optimizing power distribution and minimizing energy waste. Current systems lack the predictive intelligence to adapt to dynamic driving conditions, leading to suboptim…
- College: College of Engineering (COE)
- Inventors: Safder, Ahmad Hussain; Ahmed, Qadeer; Hanif, Athar
- Licensing Officer: Ashouripashaki, Mandana
Vulnerability and Attackability analysis of automotive controllers using structural model of the system
TS-063239 — The Ohio State University has developed a vulnerability analysis technique for connected and autonomous vehicles that assesses the vulnerability and attackability of the automotive controllers to determine the security of the system.
The Need
Automated vehicle technologies improve safety, assis…
- College: College of Engineering (COE)
- Inventors: Renganathan, Vishnu; Ahmed, Qadeer
- Licensing Officer: Ashouripashaki, Mandana