Scenario-based simulation of intelligent driving functions using neural networks

The modern technological era is witnessing a significant advancement in AI related fields such as machine learning, mobile robots and autonomous vehicles. The success of such systems is immensely dependent on computer vision algorithms. The entirely software enabled intelligent vehicles will soon be hitting the roads in the coming decade. Such self-controlled mobile robots may still be vulnerable to accidents or crashes. Therefore, the industry requires some state of the art techniques to substantiate the safety and protection of its passengers as well as other road users. Simulation based testing methods have been in use from a long time, but the newer smart vehicle innovations require better versions of simulation techniques. We thus provide an improved mechanism for simulation testing to validate safe navigation of cars in variety of traffic scenarios. Neural network approach integrated with agent based modelling is described in this paper.