A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Dynamic Time Warping <s> Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Dynamic Time Warping <s> In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system. <s> BIB002
In time series analysis, DTW is an algorithm for measuring similarity between two temporal sequences which may vary in time or speed. Dynamic Time Warping is a vital tool to utilise in driver behaviour studies. Examples of DTW implementation can be found in BIB002 that follows the following system flow in order to predict driver risk profile knowledge. The driving data is fed to a smoothing algorithm after which manoeuvre detection is performed. This is followed by a warping algorithm (DTW), and a Bayesian classifier performs the classification of a risky or safe driver. A driver safety awareness application is implemented in BIB001 , that performs DTW to detect, recognise and record these safety critical actions. A distance function is defined, and the sum of the distances along the warping path p describes the total cost c p of the alignment path. The cost function used in BIB001 is 