Denis Gingras

Papers

Total Citations

177

H-Index

About

Denis Gingras is a leading authority in intelligent transportation systems and autonomous vehicle navigation, with a career dedicated to advancing real-world robotics and vehicular intelligence. His most influential work, the 2017 paper on a "Modified artificial potential field method for online path planning applications," has garnered 177 citations by solving a critical flaw in standard APF algorithms—the local minima trap that stalls autonomous navigation. This contribution has become foundational for mobile robots and intelligent vehicles, enabling smoother, more reliable local path planning in dynamic environments. Beyond this landmark study, Gingras’s research spans sensor fusion, obstacle avoidance, and adaptive control systems for self-driving cars. His impact is reflected in the widespread adoption of his methods by both academic labs and industry developers, bridging the gap between theoretical robotics and practical deployment. A sought-after collaborator and mentor, Gingras continues to shape the next generation of autonomous systems, ensuring safer and more efficient mobility through his innovative, application-driven approach.