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About

Madhur Behl is a leading researcher at the intersection of autonomous systems, robotics, and artificial intelligence, with a focus on creating intelligent, real-world deployable solutions. His work spans autonomous electric vehicle (EV) charging infrastructure, high-speed autonomous racing, and indoor robotic navigation. Behl’s most cited paper, "Autonomous Electric Vehicle Charging System" (27 citations), addresses the critical gap between EV adoption and charging infrastructure, proposing scalable solutions to improve user experience. He also pioneered "DeepRacing," a novel end-to-end framework for parameterized trajectory planning in autonomous racing, pushing the boundaries of high-speed decision-making under uncertainty. In multi-agent racing, his "Threading the Needle" framework tackles the complex challenge of safe overtaking maneuvers. Beyond research, Behl is deeply committed to education, as shown in his work on modular small-scale hardware for teaching autonomous systems, bridging theory and hands-on practice. His contributions to safety testing for autonomous vehicles using reinforcement learning further underscore his impact on reliable AI deployment. With a growing citation record and a focus on both foundational algorithms and practical hardware, Behl is shaping the next generation of autonomous systems that are safer, faster, and more accessible.