Abstract
Existing vehicle navigation systems, such as those commonly found in smartphones and embedded in-vehicle infotainment systems, primarily focus on optimizing routes based on minimizing travel time or distance. Some systems offer options for maximizing fuel efficiency (sustainability) or avoiding tolls. These systems incorporate real-time traffic data to avoid congestion and may account for known road closures or major incidents.
Drivers currently lack tools that proactively guide them towards routes that are statistically safer based on historical data and real-time indicators of driving behavior. Data related to accident frequency and severity, near-miss events (inferred from driving patterns), and indicators of aggressive driving or "road rage" exist or can be derived but are not systematically integrated into mainstream route planning algorithms for the explicit purpose of generating demonstrably safer route options. The proliferation of vehicle sensors (accelerometers, gyroscopes, GPS, cameras, radar/lidar) and connected car technologies presents an opportunity to gather granular data relevant to road safety conditions beyond simple traffic speed.
This disclosure proposes a navigation system where safety is a primary criterion for route selection.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Nidhi, Priyam; Mouton, Jacques; Ravindranath, Radhika; Piacentini, Tom; and Black, James, "Providing Safety-Optimized Vehicle Navigation", Technical Disclosure Commons, (July 02, 2025)
https://www.tdcommons.org/dpubs_series/8302