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.