Novel Collision Warning System using Neural Networks

Abstract

Recently, there are many researches on active safety system of intelligent vehicle. To reduce the probability of collision caused by driver's inattention and mistakes, the active safety system gives warning or controls the vehicle toward avoiding collision. For the purpose, it is necessary to recognize and analyze circumstances around. In this paper, we will treat the problem about collision risk assessment. In general, it is difficult to calculate the collision risk before it happens. To consider the uncertainty of the situation, Monte Carlo simulation can be employed. However it takes long computation time and is not suitable for practice. In this paper, we apply neural networks to solve this problem. It efficiently computes the unseen data by training the results of Monte Carlo simulation. Furthermore, we propose the features affects the performance of the assessment. The proposed algorithm is verified by applications in various crash scenarios.

Study on Collision Risk during V2V-based Lane Changes using Minimum Collision Avoidance Distance

“…The method to determine the risks may vary depending on the applied system. Collision risk determination methods using neural networks [4] and using the minimum safety spacing (MSS) method [5] have been investigated. However, the above studies were limited by the sensor detection range because risks were determined using sensors.…”