Rapid-Response Framework for Defensive Driving Based on Internet of Vehicles Using Message-Oriented Middleware

Abstract

With the rapid development of automatic driving and advanced driver assistance systems, vehicle safety has improved greatly. These systems mainly use sensors installed on the vehicle and help drivers deal with human operation errors. Traffic accidents are inherently unpredictable, and it is difficult to prevent mistakes made by others. Therefore, the concept of defensive driving has attracted much interest. Defensive driving aims to increase drivers' self-awareness to prevent accidents. Future self-driving vehicles should integrate defensive driving to improve driver safety. This paper proposes a framework based on the risk evaluation value of defensive driving that rapidly transmits information about high-accident-likelihood zones to drivers or vehicles by using Internet of Vehicles technology. This should enable drivers or self-driving vehicles to predict risks and operate vehicles safely. To send alert messages in a timely manner, it is essential to overcome the challenge of processing real-time data during driving. We design five kinds of services in this rapid response framework, including raw data receiver, warning area decision, accident pattern recognition, message generator, and user profile to analyze driver information using distributed system architecture. Message-oriented middleware is used for communication between services. This framework identifies high-accident-likelihood zones by using density-based spatial clustering of applications with noise, simplifying the process of association calculation. After the calculation, this framework uses the weighted severity index to weight and compare risk severities. According to our experimental results, the service-oriented middleware design increases the speed and stability of information transmission.

Keywords

• Apriori
• DBSCAN
• defensive driving
• Internet of Vehicles
• message-oriented middleware