Artificial intelligence techniques for driving safety and vehicle crash prediction

Abstract

Accident prediction is one of the most critical aspects of road safety, whereby an accident can be predicted before it actually occurs and precautionary measures taken to avoid it. For this purpose, accident prediction models are popular in road safety analysis. Artificial intelligence (AI) is used in many real world applications, especially where outcomes and data are not same all the time and are influenced by occurrence of random changes. This paper presents a study on the existing approaches for the detection of unsafe driving patterns of a vehicle used to predict accidents. The literature covered in this paper is from the past 10 years, from 2004 to 2014. AI techniques are surveyed for the detection of unsafe driving style and crash prediction. A number of statistical methods which are used to predict the accidents by using different vehicle and driving features are also covered in this paper. The approaches studied in this paper are compared in terms of datasets and prediction performance. We also provide a list of datasets and simulators available for the scientific community to conduct research in the subject domain. The paper also identifies some of the critical open questions that need to be addressed for road safety using AI techniques.

Appendix

Reference	Research direction	Techniques used	Theory or application	Comparison with other techniques
Veeraraghavan et al. (2005)	Monitoring of the driver activities using camera. Analyzing the images from videos for the detection of safe and unsafe actions based on skin-color segmentation	Unsupervised method—agglomerative clustering, supervised method—Bayesian eigen-image classifier	An application in the area of interior vehicle design, which helps to improve the placement of controls in order to reduce unsafe driving behaviors	No comparison with other techniques is performed. However, the accuracy of the classifier is reported using the test data
				An accuracy of 95.54 % is achieved for safe driving activity, and 73.91 % accuracy is reported for the unsafe driving activity
Dixon et al. (2005)	To develop a system that minimizes the impact of untimely interruptions by providing a physical context to the driving conditions	Gradient-descent approach, GA	The result of the supervised-learning algorithm is a step towards building a system that can identify potentially tough driving conditions	No comparison with other techniques is performed. Results of gradient-descent learning and GA are compared with each other
				Gradient-descent algorithm predicted with an accuracy of 95 %, while the GA has an accuracy of 55 %
Zhou et al. (2007)	Discriminative learning approach for fusing multichannel sequential data to detect the unsafe driving patterns	CRF	Application to detect unsafe driving patterns from multi-channel data	A comparison is performed with HMM, and SVM with RBF kernel
				CRF does not require labeling of all data and uses both labeled and unlabeled data for training. It outperforms the simple discriminative classifier (SVM) and generative model (HMM) with an accuracy of 0.081 based on P$(\hbox {A}{\vert }\hbox {U})$
Tawari and Trivedi (2011)	To model the individual driving behavior in order to identify features that may be used in grouping the drivers	ANN using the MLP network and a statistical method based on GMM	Application of GMMs, FNN	Comparison is reported with NN and, statistical method based on the GMM
				MLP is relatively better but the network has significantly longer period needed for training as compared to the GMM
Rygula (2009)	Analyze the driver’s speed profile using techograph and use the same for identifying the driver style	Intensity of speed profile change graph, techograph	Theoretical and application	No comparison with other techniques. However, a comparison of the speed profile changes is done on the common roads
Tambouratzis et al. (2010)	Analysis is performed on the dataset collected by the Republic of Cyprus Police using combination of PNN’s and DT’s to investigate the potential of predicting accident severity (light, serious or fatal) from the collected parameters	PNN and DT	Application	A comparison is reported between, ANN, DT, and SVM.
				Severity prediction accuracy of the proposed methodology is superior to all previous techniques with an accuracy of 95.9307 %
Wang et al. (2010b)	Driving danger level prediction is proposed that uses multiple sensory inputs	HMM, CRF, reinforcement learning	Theoretical	A comparison between HMM, CRF and reinforcement learning is performed where, reinforcement learning outperforms other approaches
Wang et al. (2012)	A generic model has been established with a set of parameters to capture individual driver characteristics	A RLS self-learning algorithm has been developed for determining the model parameters	Application	Comparison of typical adaptive cruise control and self-learning adaptive cruise control in manual braking is performed
Singh and Dongre (2012)	Analysis of the driver profile in done for the prediction of driver suitability for driving. Drivers are categorized into following three categories: fit, unfit and partially fit	PCA, HMM	Application named CPS for mobile device with android operating system	No comparison is reported
Ali et al. (2013)	Predictive approaches to the problem of roadway departure prevention via automated steering and braking	MPC	Application	A comparison is performed between the braking torque applied by the intervention $\gamma$3 and that by the onboard electronic stability control system in combination with the driver
Xu et al. (2012b)	Investigate the applications of the GP model for real-time crash prediction on freeways	RF for the selection of candidate variables and GP	Application of the GP model for real-time crash prediction on freeways	Comparison is done with BLM. The prediction accuracy of the GP mode was found to be greater than that of the BLM
Akin and Akbas (2010)	To assess accidents that occur at intersections with different underlying reasons attributed to time of occurrence, weather and surface conditions, and user and vehicle characteristics	ANN trained by back propagation	Application	No comparison with other approaches is performed. However, a sensitivity analysis of the design parameters is reported
You et al. (2012)	CarSafe fuses information from both cameras and other embedded sensors on the phone—such as the GPS, accelerometer and gyroscope—to detect and alert the driver about dangerous driving conditions in and outside the car	Image processing	Car Safe App for Android operating system based phones	No comparison is reported
Imkamon et al. (2008)	Detection of unsafe driving patterns using data from three different sensors (Accelerometer, Camera and OBD reader)	KLT algorithm, Fuzzy logic	Application	Comparison is done with the ground truth using questionnaire
Shaout and Bodenmiller (2011)	Primary objective of this research is to capture, measure, and warn users of unsafe and inefficient driving using data from ECU by OBD-II reader	Direct measurements reading and by setting a threshold value to detect the unsafe and inefficient driving	Android based mobile application	No comparison is reported
Liu (2007)	Statistical analysis of vehicle occupancy rates using the accident data with respect to their geographic, temporal, and vehicle coverage design. Investigation and identification of three potential factors namely, accident severity, driver age, and driver gender	Average vehicle occupancies	Application	A comparison of countywide AVOs, countywide AVOs for different facility and AVOs from the field and from accidents is performed
Ning et al. (2008)	Use of a danger level function (expected negative reward) to alert the user in advance about a dangerous situation	TD learning	Application	Hard and soft label approaches are compared with our TD learning method
Jabon et al. (2011)	An active driver-safety framework that captures both vehicle dynamics and the driver’s face. A bottom up approach which uses 22 raw facial features, time and frequency domain statistics to determine the most valuable statistics for accident prediction	DWT, Bayesian nets, decision tables, decision trees, SVMs, regressions, and LogitBoost	Application	No comparison with other techniques is reported. However, the ROC curves to analyze the major accident is given. ROC depicts true versus false positives for the classifiers
Li et al. (2008)	To evaluate the application of SVM models for predicting motor vehicle crashes	SVM models based on statistical learning theory	Application	A comparison with BNN is made
				SVM model is faster than neural network models
				The training of neural networks is usually computationally intensive
Ning et al. (2009)	Detection of unsafe system states based on the analysis of multi-sensor data streams	TD learning	Application	A comparison is performed with logistic regression and general linear regression
Chong (2004)	Modeling the severity of injury resulting from traffic accidents using ANNs and DTs	ANN and DT	Application	No comparison is performed with other approaches. However, the ANN and DTs are compared with each other for accuracy
Xie (2007)	Application of BNN models for predicting motor vehicle crashes. A series of models are estimated using data collected on rural frontage roads in Texas	BNN Models	Application	A comparison is performed with negative Binomial regression model
				Neural network models perform better than the NB regression model in terms of data prediction
Bundele and Banerjee (2009)	A system to monitor the fatigue/drowsiness/stress level of a driver using physiological parameters	ANN	Application	Comparing NN with different number of layers
Manan (2011)	Analysis of the accident data to determine the location of accident at intersection with the highest rank of accident point weightage and to identify the causes of accidents occurred	MLR	Theoretical	No comparison reported
Guo et al. (2009)	Monitoring and analyzing the fatigue and attention state of driver by using the features of face orientation	Face orientation	Application and theoretical	No comparison reported
Gong and Yang (2011)	Pattern recognition of drivers’ behavior before accidents	Fuzzy logic based on multiple regression theory, multi-objective decision theory	Application	No comparison reported
Takatori and Hasegawa (2004)	Influence of prediction methods in the driving assistance system	Linear Prediction	Application	Comparison is done via influence of the system prediction time on the average accident interval
Ma et al. (2012)	Detection of unsafe driving states is presented. The detection is based on the multi sensor approaches, including gyrometer, accelerometer, radar and videos	Unsupervised learning algorithm to perform the unsafe states detection	Application	No comparison reported
Damousis et al. (2007)	Development of physiological algorithms for real-time, unobtrusive, sleepiness-related prediction for time critical operations, such as driving within sensation	GA, Fuzzy expert system	Application	Comparison is reported with a EOG-based sleep prediction algorithm and the proposed approach provides more than 90 % prediction accuracy
Yuejing et al. (2010)	Establishing a functional relationship between accident forms and influencing factors. Provides basis for the screening of safety degree and targeted reasonable reconstruction of the intersection	ANN	Theoretical	No comparison reported
Hu et al. (2004)	Probabilistic model for predicting traffic accidents using three-dimensional model-based vehicle tracking is proposed	Fuzzy self-organizing neural network algorithm	Application	No comparison with other approaches is reported. However, a comparison between various structures of NNs is listed
Rujun and Xiuqing (2010)	Study on traffic accident prediction models. The RBF neural network model used to predict and extrapolate the number of fatalities	RBF Neural Network model	Application	RBF NN has a simple structure, concise training, quick convergence study speed and also have a strong advantage in the approximation ability, classification and speed over BP network. Predictive values of the network are closer to the actual one
Jeong et al. (2004)	Using local adaptive threshold and local probability for detecting driving region and the regions where driving is possible	Adaptive threshold method and local-probability	Application	A comparison based on the ability of extension between prevailed contour extension and local probability is reported
Lv et al. (2009)	Identification of traffic conditions leading to traffic accidents based on the data collected from software simulator	SNM	Theoretical	No comparison reported
Quintero et al. (2012)	Modeling of driving behaviors for identification of different types of drivers, and identify high risk areas on the roads	ANN	Application	No comparison reported
Murphey et al. (2009)	Developing a new algorithm for the classification driving style by analyzing the jerk profile of the driver	DS Classification	Theoretical	Comparison of the proposed approach is reported with an acceleration based algorithm