Chris Brace
Papers
3
Total Citations
42
H-Index
3
About
Chris Brace is a researcher focused on intelligent transportation systems, driver behavior modeling, and sensor fusion for autonomous driving. His major contributions lie in developing computational methods to classify and model driving styles, aiming to improve fuel efficiency and road safety. In his most-cited work, "A Support Vector Clustering Based Approach for Driving Style Classification" (21 citations), he introduced a novel clustering technique to identify habitual driving patterns, enabling the extraction of economical and ecological driving behaviors. He further advanced this area with "Driving Style Modelling with Adaptive Neuro-Fuzzy Inference System and Real Driving Data" (6 citations), demonstrating how adaptive systems can capture nuanced driver variability. Brace also contributed to autonomous vehicle perception through "Distance Estimation by Fusing Radar and Monocular Camera with Kalman Filter" (15 citations), proposing a low-cost, accurate approach for distance estimation by fusing radar and camera data—critical for driver modeling and accident avoidance. His work bridges machine learning, control systems, and real-world driving data, offering practical solutions for eco-driving and autonomous navigation. With growing citation impact, Brace’s research is shaping how we understand and optimize human-vehicle interaction.
Research Focus
Key Achievements
Top Papers
- 1A Support Vector Clustering Based Approach for Driving Style Classification21 citations · 2019
- 2Distance Estimation by Fusing Radar and Monocular Camera with Kalman Filter15 citations · 2017
- 3