Artificial intelligence techniques for driving safety and vehicle crash prediction


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.

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Correspondence to Zahid Halim.



Reference Research direction Techniques used Theory or application Comparison with other techniques
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     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\((\hbox {A}{\vert }\hbox {U})\)
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     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
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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
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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
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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
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     Neural network models perform better than the NB regression model in terms of data prediction
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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
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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

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Halim, Z., Kalsoom, R., Bashir, S. et al. Artificial intelligence techniques for driving safety and vehicle crash prediction. Artif Intell Rev 46, 351–387 (2016).

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  • Road safety
  • Accident prediction
  • Artificial intelligence techniques
  • Traffic datasets and simulators