Driving Behavior Analysis Software for AI-Driven Autonomous Vehicles

Accepting autonomous vehicles as a reliable and safe transportation service requires the realization of smooth, natural, human-like vehicle control. The plethora of driving data captured from modern cars is a key enabler for solving this problem. The intelligent analysis, filtering and modulation of human driving behavioral data for improving the passenger ride and safety experience is discussed in this paper. The core idea of the proposed solution is the automatic extraction of driving features followed by the conditioning and balancing of selected key features and driving attributes used to train machine learning models responsible for vehicle motion planning and control. For this task, a DRIVing Behavior Analysis Software (DRIVBAS) was developed with the purpose of increasing the efficiency and the transparency of data analytics and machine learning activities as applied to autonomous vehicles. The overall functionality and implementation of the proposed solution is demonstrated with real-world case study results.