Simulation and time-series analysis for Autonomous Emergency Braking systems

Abstract: One central challenge for Autonomous Driving (AD) systems is ensuring functional safety. This is affected by all parts of vehicle automation systems: environment perception, decision making, and actuation. The AD system manages its activity towards achieving its goals to maintain in the safety domain, upon an environment using observation through sensors and consequent actuators. Therefore, this research investigates the operational safety for the AD system. In this research, a simulation for the Autonomous Emergency Braking (AEB) system and a simple scenario are constructed on CARLA, an open-source simulator for autonomous driving systems, to investigate the factors that impact the performance of the AEB system. The time-series data that influence the AEB are collected and fed into three time-series analysis algorithms, Autoregressive Integrated Moving Average model (ARIMA), regression tree and Long short-term memory (LSTM), to select a suitable time-series algorithm to be used for the AEB system. The results show that weather, the measurement range of the sensors, and noise can affect the results of the AEB system. After comparing the performance of these three time-series algorithms through contrasting the recall and precision of these three algorithms to detect noise in the data, the results can be obtained that LSTM has the better performance for long-term analysis. And ARIMA is more suitable for short-term time-series analysis. LSTM is chosen to analyze the time-series data, since the long-term time-series analysis is necessary for the AEB system and it can detect the noise in the variables of the AEB system with better performance.