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About

Joshua Siegel is a leading researcher in autonomous systems, with a primary focus on path planning and self-driving vehicle technologies. His most influential work, “A Survey of Path Planning Algorithms for Mobile Robots” (2021, 373 citations), provides a comprehensive taxonomy of algorithms used by mobile robots, UAVs, and autonomous cars to identify safe, efficient, and collision-free travel paths. This survey has become a foundational resource for researchers and engineers working on autonomous navigation. Siegel also advances practical self-driving education through his development of a gamified simulator and low-cost physical platform, where game mechanics encourage high-quality data capture and environmental domain randomization improves data generalizability. His work on vehicular lane-keeping algorithms, developed collaboratively with undergraduate researchers, demonstrates his commitment to hands-on, team-based engineering education. More recently, Siegel has explored vehicle-to-everything (V2X) communication using roadside units to provide over-the-horizon object awareness, addressing critical sensor limitations in automated vehicles. Through these contributions—ranging from foundational surveys to accessible training platforms and advanced communication systems—Siegel is shaping both the theoretical understanding and practical deployment of autonomous vehicle technologies.