Nowadays, Advanced Driver Assistance Systems (ADAS) play important roles in improving the safety of driving. ADASs along with new safety standards are now essential to improve the safety of automated driving. These standards differ from the safety standards in other industries because an autonomous vehicle rides in a very intricate stochastic environment where many details should be considered to maintain the safety. From one point of view, autonomous car failures are divided into two categories: hardware and software failures. According to the literature, if the reason of fault belongs to hardware, the troubleshooting will relatively be easier than software failure. In other words, if the fault is the subset of software, that would be more challenging task to detect potential fault and recover from the failure. In this paper, we propose a new approach to detect the software failures, which may be unpredictable using a typical safety protocol. A predictive machine learning method is developed to improve decision making process in order to avert the potential accidents occurred by software defects. The proposed method evaluates detected obstacle in the span of a specified number of frames to discern whether objects are detected correctly or not. Hence, it provides extra time to choose the suitable action before it reaches the danger, which includes lane changes, cut-in maneuvers, stopping the car, accelerating the car, slowing down, and allowing vehicles to pass. Results show that the new approach improve the safety by increasing the precision of failure detections.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Software Failures Prediction in Self-Driving Vehicles


    Contributors:


    Publication date :

    2020-11-01


    Size :

    995398 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Improving Safety of Automated Driving by Minimizing Software Failures in Self-Driving Vehicles

    Zadeh, Mehrdad / Fekri, Pedram / Abedi, Vajiheh et al. | SAE Technical Papers | 2019


    Analysis of failures in railway driving vehicles

    Sojka, J. / Mencik, J. | Tema Archive | 2002


    METHODS OF HANDLING SENSOR FAILURES IN AUTONOMOUS DRIVING VEHICLES

    HU JIANGTAO / JIANG YIFEI / LI DONG et al. | European Patent Office | 2019

    Free access

    METHODS FOR HANDLING SENSOR FAILURES IN AUTONOMOUS DRIVING VEHICLES

    HU JIANGTAO / JIANG YIFEI / LI DONG et al. | European Patent Office | 2019

    Free access

    Methods for handling sensor failures in autonomous driving vehicles

    HU JIANGTAO / JIANG YIFEI / LI DONG et al. | European Patent Office | 2022

    Free access