Fault Tolerance analysis of an Adaptive Neuro-Fuzzy Inference System for mandatory lane changing decisions in automated driving

Abstract

Past research has developed a binary decision model for mandatory lane changes based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). This ANFIS Decision Model (simply called ADEM), developed and tested with the Next Generation Simulation (NGSIM) data, mimics the sensory inputs and decisions of human drivers. This research assumed that ADEM will be implemented as part of the automated lane changing system in Automated Vehicles (AVs). The system in AVs will depend on active radar sensors to make measurements. The sensor outputs will be converted into the input parameter values of ADEM. This research tested ADEM’s performance when the sensors could only measure the distance of surrounding vehicles within 50m, and when one of the sensors malfunctions. The original NGSIM test data set was modified to simulate the sensors’ detection range limit in Scenario 0, plus six other scenarios in which each sensor took turns to fail and assumed either the minimum or maximum possible output values. The results show that: (i) ADEM performs in a safer manner when considering the sensors’ limited detection range; (ii) the minimum value of 0m should be used as the default sensor output when a sensor fails, so that ADEM makes safer mandatory lane changing decisions; and (iii) the most critical sensors, by which failure of any of them would cause the greatest degradation to ADEM’s performance, are the two sensors that measure the distances to the preceding and the following vehicles in the target lane.

Keywords

• Mandatory Lane Change
• Adaptive Neuro-Fuzzy Inference System
• Fault Tolerance
• Automated Vehicle
• Safety