Accident risk prediction and avoidance in intelligent semi-autonomous vehicles based on road safety data and driver biological behaviours¹

Article type: Research Article

Authors: Ahmad, Shabir^{; *} | Jamil, Faisal^{; 2} | Khudoyberdiev, Azimbek^{; 3} | Kim, DoHyeun^{; *}

Affiliations: Department of Computer Engineering, Jeju National University, Jeju, Korea

Correspondence: [*] Corresponding author. Shabir Ahmad and DoHyeun Kim, Department of Computer Engineering, Jeju National University, Jeju, Korea. E-mail: shabir@jejunu.ac.kr (Shabir Ahmad); E-mail: kimdh@jejunu.ac.kr (DoHyeun Kim).

Note: [1] This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M3F2A1073387), and this research was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-01456, AutoMaTa: Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT). Any correspondence related to this paper should be addressed to Dohyeun Kim.

Note: [2] E-mail: faisal@jejunu.ac.kr

Note: [3] E-mail: azimbekkhudoyberdiev@jejunu.ac.kr

Abstract:  Autonomous vehicles technology is an emerging area and has attracted lots of recognition in recent times. Accidents-free driving has always been the focal point of autonomous vehicles. Autonomous vehicles have the potential to eliminate human errors while driving, which has been argued as the predominant cause of traffic accidents. In autonomous vehicles technologies, a variety of efforts have been made to eliminate human drivers. The full elimination of humans is not possible at this moment, but some of the tasks can be automated to facilitate the drivers. In this paper, we investigate the leading causes of accidents based on UK vehicle safety data of 2017-2018. We analyze the data and investigate the leading factors which cause traffic crashes. Based on the leading features in the dataset, we then run different prediction algorithms to predict the severity of accidents under a given input feature set. The accuracy of the model with Decision Tree classifier, Random Forest, and Logistic Regression are compared, and it has been found that Random Forest performs best among others with 95% accuracy. The trained random forest model is deployed on the Internet of Things server based on Arduino, and a lightweight application is developed to get the vital data from the driver. The data is applied to the trained model to predict the risk index of driving. This application is lightweight but yet provide a significant contribution in terms of safety in autonomous vehicles.

Keywords: Internet of things, cyber-physical systems, real-time systems, virtualisation, virtual object

DOI: 10.3233/JIFS-191375

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4591-4601, 2020