Reliability assessment of autonomous vehicles based on the safety control structure

Author(s):  
Feipeng Wang ◽  
Diana Filipa Araújo ◽  
Yan-Fu Li

The recent social trends and accelerated technological progress culminated in the development of autonomous vehicles (AVs). Reliability assessment for AV systems is in high demand before its market launch. In safety-critical systems (SCSs) such as AV systems, the reliability concept should be broadened to consider more safety-related issues. In this paper, reliability is defined as the probability that the system performs satisfactorily for a given period of time under stated conditions. This paper proposes a reliability assessment framework of AV, consisting of three main stages: (i) modeling the safety control structure through the Systems-Theoretic Accident Model and Processes (STAMP); (ii) mapping the control structure and functional relationships to a directed acyclic graph (DAG); and (iii) construct a Bayesian network (BN) on DAG to assess the system reliability. The fully automated (level 5) vehicle system is shown as a numeric example to illustrate how this suggested framework works. A brief discussion on involving human factors in systems to analyze lower levels of automated vehicles is also included, demonstrating the need for further research on real case studies.

Author(s):  
Alexander Yasko ◽  
Eugene Babeshko ◽  
Vyacheslav Kharchenko

The complexity of modern safety critical systems is becoming higher with technology level growth. Nowadays the most important and vital systems of automotive, aerospace, nuclear industries count millions of lines of software code and tens of thousands of hardware components and sensors. All of these constituents operate in integrated environment interacting with each other — this leads to enormous calculation task when testing and safety assessment are performed. There are several formal methods that are used to assess reliability and safety of NPP I&C (Nuclear Power Plant Instrumentation and Control) systems. Most of them require significant involvement of experts and confidence in their experience which vastly affects trustworthiness of assessment results. The goal of our research is to improve the quality of safety and reliability assessment as result of experts involvement mitigation by process automation. We propose usage of automated FMEDA (Failure Modes, Effects and Diagnostic Analysis) and FIT (Fault Insertion Testing) combination extended whith multiple faults approach as well as special methods for quantitative assessment of experts involvement level and their decisions uncertainty. These methods allow to perform safety and reliability assessment without specifying the degree of confidence in experts. Traditional FMEDA approach has several bottlenecks like the need of manual processing of huge number of technical documents (system specification, datasheets etc.), manual assignment of failure modes and effects based on personal experience. Human factor is another source of uncertainty. Such things like tiredness, emotional disorders, distraction or lack of experience could be the reasons of under- and over-estimation. Basing on our research in field of expert-related errors we propose expert involvement degree (EID) metric that indicates the level of technique automation and expert uncertainty degree (EUD) metric which is complex measure of experts decisions uncertainty within assessment. We propose usage of total expert trustworthiness degree (ETD) indicator as function of EID and EUD. Expert uncertainty assessment and Multi-FIT as FMEDA verification are implemented in AXMEA (Automated X-Modes and Effects Analysis) software tool. Proposed Multi-FIT technique in combination with FMEDA was used during internal activities of SIL3 certification of FPGA-based (Field Programmable Gate Array) RadICS platform for NPP I&C systems. The proposed expert trustworthiness degree calculation is going to be used during production activities of RPC Radiy (Research and Production Corporation). Our future work is related to research in expert uncertainty field and extension of AXMEA tool with new failure data sources as well as software optimization and further automation.


2020 ◽  
Vol 128 ◽  
pp. 106393
Author(s):  
Xingyu Zhao ◽  
Kizito Salako ◽  
Lorenzo Strigini ◽  
Valentin Robu ◽  
David Flynn

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ming Li ◽  
Nan Zheng ◽  
Xinkai Wu ◽  
Weihua Li ◽  
Jianhua Wu

With the potential of increasing mobility and reducing cost, shared mobility of autonomous vehicles (AVs) is going to gain solid growth in the coming decade. The major issue for the shared use of AVs is how to project serving routes in an efficiently way. From another perspective, this issue could be understood as to segment maximum number of passengers into groups. Therefore, this paper intends to investigate passengers’ similarity instead of directly matching AVs and passengers. The goal is to determine the minimum number of groups and assign each group with an AV. To this end, a cluster-based algorithm is proposed to classify passengers. Numerical experiments with both small-size and large-size demands are performed to present the validity of the proposed algorithm. Results indicate that the cluster-based algorithm could bring benefit to minimizing the number of vehicles and total travel distance. At last, sensitivity analysis of key parameters shows that vehicle capacity will have little impact when the number of seats exceeds four, and time windows could make continuous influence on gathering passengers.


2018 ◽  
Vol 882 ◽  
pp. 90-95 ◽  
Author(s):  
Michael Scholz ◽  
Xu Zhang ◽  
Jörg Franke

The paper presents an intralogistics routing-service for autonomous and versatile transport vehicles. An infrastructural sensor digitize the workspace of the vehicle and is the basis for the vehicle-specific routing plan. Nowadays, a central computing unit allocates transportation task to a known number of automated guided vehicles, which are usually of the same type. Furthermore, this device generates a routing appropriate to the dimensions and the kinematic gauge of the vehicle fleet. The pathing for each specific vehicle is calculated and the result is send to the different entities. The approach of this paper bases on the digitization of the workspace with a ceiling camera, which divides the scenery into moving obstacles and an adaptive background picture. A central computing unit receives the background picture of several cameras and stitch them together to an overview of the entire workspace, e.g. a production hall. Furthermore, the approach includes the development of automated guided vehicles to versatile autonomous vehicles, were each entity is able to calculate the pathing on a given routing plan. A fleet of versatile autonomous vehicles consists of vehicles with task-specific dimensions and kinematic gauges. Therefore, each vehicle needs its own routing-plan. The solution is that each vehicles uses a vehicle parameter-server and register itself with these parameters at the routing unit. This unit is calculating a routing-plan for each specific vehicle dimension and gauge and providing it. When getting a new task, the vehicles uses this routing-plan to do the pathing. The routing-algorithm is implemented inside the service-layer of the versatile autonomous vehicle system. This approach lowers the amount of data, which is send between the service layer and the transportation entities by reducing the information of the workspace to the possible routes of each specific vehicle. Furthermore, the calculation time for routing and pathing is lowered, because each vehicle is calculating its task-specific path, but the route-map is calculated once for each vehicle-type by the routing-service.


Author(s):  
Paul S. Fancher ◽  
Zevi Bareket

A model for studying and evaluating the performance of drivers in controlling headway situations is currently being used to better understand how a driver’s perception of headway range and its rate of change in time (range rate) influence the performance of the driver-vehicle system in freeway driving situations. The model is based upon ideas derived from vehicle dynamics, control theory, and human factors research. It is an interpretive model in the sense that results obtained during real driving are processed to evaluate the parameter values and functional relationships used in the model. In this way, the model evolves as new data and information become available and as calculated results are interpreted and understood.


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