Dynamic Driving Style Recognition for Human Machine Shared Control

An intelligent human machine shared control system should be able to identify the driving style of real human drivers, so that the recommended driving strategy is in line with their driving style, and thus reduce the human intervention during the human-machine co-driving. The upgrade of vehicle sensors provides us an opportunity for a better judgement of the driver's driving style. This paper proposes a new driving style classification method based on the active learning. This method first uses the concept of driving volatility measurement to measure the performance of vehicles in terms of speed and acceleration, and then generates data to reduce the influence of data distribution on learning effect. The proposed method divides drivers' behaviors into aggressive, ordinary and calm styles. It can effectively learn the features related to driving style in driving volatility measurement, and the active learning method itself can reduce the requirement of human expert experience. The effectiveness of the proposed method is proved by the experiments on the UAH-Driveset, and then this paper uses the proposed method to analyze some data from the security pilot model deployment (SPMD).