Cut-in Critical Level Prediction via Simulation Based Time-to-Collision Algorithm

Cut-in critical level prediction is of vital importance to Advanced Driving Assistance system (ADAS) to fulfil regional requirements and to increase safety. Time-to-Collision is the key component for critical levels of cut-in scenario. Hence, a new simulation based Time-to-Collision (TTC) calculation algorithm is firstly introduced in this paper. For the purpose of cut-in critical level prediction, the values of TTC in c.a 2000 cut-in cases are calculated, which are used to train a novel machine learning based cut-in critical level prediction method. The goal of ADAS functions development is to perform as a sophisticated driver, especially in dealing with risks. Thus, the correlation coefficient between ego vehicle deceleration and TTC could be used to evaluate the performance of different TTC calculation methods. In order to validate the superiority of simulation based TTC calculation algorithm, the Pearson correlation coefficient is calculated for the simulation based TTC and the TTC calculated by the traditional method, which are 0.7882 and 0.1357, respectively. Through enough valid regional cut-in samples trained prediction algorithm, TTC could be estimated accurately and effectively, i.e., the accuracy reaches 92%. To the best of the author’s knowledge the simulation based TTC calculation method and the cut-in critical level prediction learning algorithm are new contributions in ADAS field.