Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Abstract

When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper.

Keywords

• collision risk
• time-to-collision
• autonomous vehicles
• machine learning