This technology consists of algorithms created using physics-based driving models and deep neural networks to predict driver behavior
Current technologies to predict driver behavior use either physics-based models, which consist of assumptions that over-simplify results, or data-driven models, which require large datasets that can be expensive and time-consuming to acquire. As a result of these shortcomings, there is currently no standard method to accurately, efficiently and cost-effectively predict driver behavior.
This technology consists of machine learning algorithms that combine physics-based models with historical driving data and deep neural networks. The algorithms are coded in the TensorFlow/Python platform, and can be packaged for use in various programming languages such as R and Python. This allows for integration into other applications to predict driving maneuvers and to identify abnormal driving behavior.
The algorithms have been validated using the publicly available dataset NGSIM.
IR CU22032
Licensing Contact: Dovina Qu