A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors

Abstract

In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution) and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden) can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects.

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

“…In-vehicle camera [29][30][31][32][33], sensors, hardware [10,24] Real-world driving data; high-accuracy data; Access to driver's personal data and vehicle control data Expensive and time consuming; Lack of data in extreme and dangerous driving condition…”

“…Driving simulator [24][25][26] Collect drivers' behavior in designed and controlled driving scenarios Driving behavior observed in the simulator may not always correspond to real-world driving Traffic video [35][36][37] Low expense; Easy to collect enormous vehicle data in a short time; Observe vehicle interaction in real traffic flow This paper is organized as follows. Section 2 presents the related work on driving behavior data analysis and machine learning algorithms.…”

“…In addition, SVM performs well with a limited number of training samples, and SVM has fewer parameters to be determined [22,23]. Therefore, many studies employed SVM to build driving style recognition models [24][25][26][27][28].…”

“…Researchers used instrumented vehicles to conduct naturalist driving experiments to identify behaviors [29][30][31]. Some instrumented vehicles were equipped with in-vehicle mounted cameras to capture video images of drivers [32,33], while others got help from specialized hardware and sensors to acquire throttle opening, pedal brake, wheel steering, vehicle speed, acceleration rate, and yaw rate [10,24]. Although the driver controlling data and vehicle kinematic data can be collected on the instrumented vehicles, the requirement of expensive devices and sensors is a major obstacle to large scale naturalist driving experiments.…”

MagTrack

“…Earlier inertia-sensor-based safe driving monitoring systems mainly focus on monitoring car motion dynamics and can alert abnormal vehicle movements, such as aggressive acceleration or turning [9,38,72,74,75]. However, they often leave insufficient time for the drivers to respond to complex road situations, especially when the drivers are distracted.…”