Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
Off-road intelligent vehicle is an important application about Internet of Vehicles technology used in the transportation field, and\nthe front obstacle recognition method is the key technology for off-road intelligent vehicle. In this paper, based on smart data\naggregation inspired paradigm of IoT applications, we mainly study perception technology in vehicle networking by using image\ndata and one symmetrical speeded-up robust features detector (SURF). By considering symmetry and image data aggregation, we\nfound that data aggregation had the ability of providing global information for Internet of Vehicles systems. After we have built\nthe experiment platform, the experiment results showed that this method is faster than Scale-Invariant Feature Transform\nalgorithmin this case, which can satisfy the water detection accuracy and the real-time requirement. So, thismethod is effective for\nthe water images detection with great symmetry to off-road intelligent vehicle, and it also gives a useful reference about environment\nperception technology and smart data aggregation inspired paradigm used in future Internet of Vehicles, intelligent\nvehicle, and traffic safety applications....
In order to deeply analyze and describe the characteristics of car-following behaviour of turning vehicles at intersections, the\nfeatures and application conditions of classic car-following models were analyzed firstly. And then, through analysing the\nrelationship between the maximum velocity of car-following vehicles and the turning radius of intersection, the differences in key\nvariables between turning and straight car-following behaviour were identified. On the basis of Optimal Velocity (OV) model, a\nTurning Optimal Velocity (TOV) car-following model with consideration of turning radius and sideway force coefficient at\nintersections was developed. PreScan simulation was employed to build the scene of turning car-following process at an intersection....
The near-future deployment of high-level automation vehicles (AVs) can render promising opportunities to solve ongoing\nhindrances in modern safety-related research. Monitoring fatigued drivers on any road section is one of these challenges. Vehicle\ntrajectory big data, monitored through AVs, include key information with which to monitor fatigued drivers on roads. To mine\nthis upcoming opportunity, a new data-driven approach which allows the direct monitoring of fatigued drivers on road segments\nis proposed here for the first time. A feasible study was conducted using big vehicle trajectory data and real-life traffic accident\ndata. The results showed that fatigued drivers on a target road section can be successfully surveyed using the driving durations\nfrom departure locations to the target road section. It was found that, with a statistical correlation of 0.90, an index for fatigued\ndrivers has strong explanatory power about the traffic accident rate. This finding indicates that the proposed method will be a\npromisingmeans by which tomonitor fatigued drivers at road locations in the upcoming era of autonomous vehicles. In addition,\nthe method is immediately practicable if vehicle trajectory data are available....
The vehicle color is considered to be a significant factor affecting driver visibility. The primary objective of this study is therefore to\ndetermine the impact of black-and-white striped vehicles (BWVs) on driver visibility through simulation-based experiments. In\nthese experiments, subjects were asked to performfront and rear target identification tasks under daylight and twilight conditions...............
CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and\ncommunication components to make driving decisions. A large number of companies, research organizations, and governments\nhave researched extensively on the development of CAVs. The increasing number of autonomous and connected functions\nhowever means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber\nattacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security\nPrinciples, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this\npaper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences.\nSeverity of each type of attacks is then analysed based on clearly defined new set of criteria....
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