Search results
1 – 10 of 15Zheng Xu, Yihai Fang, Nan Zheng and Hai L. Vu
With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.
Abstract
Purpose
With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.
Design/methodology/approach
The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.
Findings
Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.
Research limitations/implications
Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.
Practical implications
This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.
Social implications
This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.
Originality/value
A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.
Details
Keywords
Abstract
Purpose
This paper aims to investigate the reliability, availability, maintenance and safety analysis method for railway network operation.
Design/methodology/approach
The reliability of the railway network is proposed based on the accident frequency and the topology of the railway network. Network efficiency and capacity are proposed to evaluate the availability of the railway network. The maintenance of the railway network is analyzed from the perspective of accident recovery time. The safety index of the railway network is proposed to measure the safety of railway stations and sections and the K-means method is proposed to find the safety critical stations and sections. Finally, the effectiveness of the proposed method is illustrated through a real-world case study.
Findings
The case study shows that the proposed model can produce a big-picture averaged view of the network-wide safety level and help us identify the safety critical stations and sections by considering both the expected reduction of network efficiency and capacity.
Practical implications
The potential application of the proposed model is to help the safety managers determine the investments in safety management of each section and station and then increase the safety and robustness of railway network operation.
Originality/value
The safety analysis of the railway network should consider the reliability, availability and maintenance of the railway network. In this paper, the reliability of the railway network is proposed based on the accident frequency and the topology of the railway network. Network efficiency and capacity are proposed to evaluate the availability of the railway network. The maintenance of the railway network is analyzed from the perspective of recovery time. Finally, the safety index of the railway network is proposed to analyze the safety critical stations and sections.
Details
Keywords
Xiang Gu, Yueting Chai, Yi Liu, Jianping Shen, Yadong Huang and Yixuan Nan
Material conscious and information network (MCIN) is a kind of cyber physics social system. This paper aims to study the MCIN modeling method and design the MCIN-based…
Abstract
Purpose
Material conscious and information network (MCIN) is a kind of cyber physics social system. This paper aims to study the MCIN modeling method and design the MCIN-based architecture of smart agriculture (MCIN-ASA) which is different from current vertical architecture and involves production, management and commerce. Architecture is composed of three MCIN-ASA participants which are MCIN-ASA enterprises, individuals and commodity.
Design/methodology/approach
Architecture uses enterprises and individuals personalized portals as the carriers which are linked precisely with each other through a peer-to-peer network called six-degrees-of-separation block-chain. The authors want to establish a self-organization, open and ecological operational system which includes active, personalized consumption, direct, centralized distribution, distributed and smart production.
Findings
The paper models three main MCIN-ASA participants, namely, design the smart supply, demand and management functions, which show the feasibility innovation and high efficiency of implementing MCIN on agriculture. At the same time, the paper presents a prototype system based on the architecture.
Originality/value
The authors think that MCIN-ASA improves current agriculture greatly and inspires a lot in production-marketing-combined electronic commerce.
Details