Abnormal Driving Behaviors Detection And Identification Using Smartphone Sensors

Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety.Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result,i.e.distinguishing abnormal driving behaviors from normal ones.To improve drivers' awareness of their driving habits so as to prevent potential car accidents,we need to consider a fine-grained monitoring approach,which not only detects abnormal driving behaviors but also identifies specific types of abnormal driv-ing behaviors,i.e.Weaving,Swerving,Sideslipping,Fast U-turn,Turning with a wide radius and Sudden braking.Through empirical studies of the 6-month driving traces collected from real driv-ing environments,we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation.Recognizing this observation,we further propose a fine-grained abnormal driving behavior detection and identification system to perform real-time high-accurate abnormal driving behaviors monitoring using smart-phone sensors.Specifically,we extract several effective features to capture the patterns of abnormal driving behaviors.After that,two machine learning method,Support Vector Machine(SVM)and Neuron Networks(NN),are employed to train the features and output a classifier model which conducts fine-grained identification.From results of extensive experiments with 20 volunteers driving for another 4 months in real driving environments,we show that our system achieves an average total accuracy of 95.36%.