Sensor-based risk perception ability network design for drivers in snow and ice environmental freeway: a deep learning and rough sets approach

Due to factors such as snow and ice impeding drivers’ vision, the number of automobile crashes significantly rises during winter months. This study sets forth an automatic evaluation network of the risk perceived ability for motorists driving on the freeway in snow and ice environments, using a deep learning approach and the rough sets technique. First, a naturalistic driving experiment involving thirteen licensed drivers was conducted on a freeway in Jilin, China, with a crash hot spot set prior to the start of the experiment. Then multi-sensor (eye-trackers, mini-cameras, and speed detectors) apparatuses, collecting both images and numerical data, were utilized. Afterward, restricted Boltzmann machine was used to develop a deep belief network (DBN) along with training procedures. Rough sets technique was added as judgment in output layer of the DBN. Finally, fixation duration, pupil size, changes in speed, etc., were used as input impact factors and the perception conditions were used as output variables to train the network. Furthermore, after comparing the DBN-based risk perception ability network with Naïve Bayes and BP-ANN (artificial neural networks with back propagations), the results indicate that the DBN-FS not only outperforms both Naïve Bayes and BP-ANN, but also improves the accuracy of perceiving risky conditions. This approach can provide reference for the design of hazard detection systems of partially automated vehicles.