scholarly journals Management of the reliability of intelligent vehicles as a method to improve traffic safety

2018 ◽  
Vol 36 ◽  
pp. 465-471 ◽  
Author(s):  
Irina Makarova ◽  
Eduard Mukhametdinov ◽  
Eduard Tsybunov
Keyword(s):  
Traffic Safety ◽  
Intelligent Vehicles

scholarly journals Multitarget Vehicle Tracking and Motion State Estimation Using a Novel Driving Environment Perception System of Intelligent Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yuren Chen ◽  
Xinyi Xie ◽  
Bo Yu ◽  
Yi Li ◽  
Kunhui Lin
Keyword(s):  
State Estimation ◽  
Traffic Safety ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Calculation Model ◽  
Intelligent Vehicles ◽  
Vehicle Tracking ◽  
Motion State ◽  
Perception System ◽  
Environment Perception

The multitarget vehicle tracking and motion state estimation are crucial for controlling the host vehicle accurately and preventing collisions. However, current multitarget tracking methods are inconvenient to deal with multivehicle issues due to the dynamically complex driving environment. Driving environment perception systems, as an indispensable component of intelligent vehicles, have the potential to solve this problem from the perspective of image processing. Thus, this study proposes a novel driving environment perception system of intelligent vehicles by using deep learning methods to track multitarget vehicles and estimate their motion states. Firstly, a panoramic segmentation neural network that supports end-to-end training is designed and implemented, which is composed of semantic segmentation and instance segmentation. A depth calculation model of the driving environment is established by adding a depth estimation branch to the feature extraction and fusion module of the panoramic segmentation network. These deep neural networks are trained and tested in the Mapillary Vistas Dataset and the Cityscapes Dataset, and the results showed that these methods performed well with high recognition accuracy. Then, Kalman filtering and Hungarian algorithm are used for the multitarget vehicle tracking and motion state estimation. The effectiveness of this method is tested by a simulation experiment, and results showed that the relative relation (i.e., relative speed and distance) between multiple vehicles can be estimated accurately. The findings of this study can contribute to the development of intelligent vehicles to alert drivers to possible danger, assist drivers’ decision-making, and improve traffic safety.

Detection and recognition method of monocular vision traffic safety information for intelligent vehicles

2020 ◽  
Vol 39 (4) ◽  
pp. 5017-5026
Author(s):  
Zhi Jin ◽  
Dong-Yuan Ge
Keyword(s):  
Information Processing ◽  
Traffic Safety ◽  
Detection Method ◽  
Safety Information ◽  
Intelligent Vehicles ◽  
Lane Detection ◽  
Monocular Vision ◽  
Detection Methods ◽  
Blind Area ◽  
Detection And Recognition

Intelligent vehicle technology has become a research hot issue in recent ten years, the reason is that intelligent vehicles can not only be used as a flexible weapon platform in the military. And in life, it is also a system that provides convenience and security for people. For example, driverless cars and advanced driver assistance systems (ADAS). Information processing is the key to the degree of intelligence, and the detection and recognition of traffic safety information based on monocular vision is the core of information processing, it’s also the bottleneck problem. Because of the complexity and diversity of the environment have brought great challenges to this problem. In this paper, the existing lane detection methods in structured and semi-structured roads do not specifically consider the problem of weak line detection, two models are proposed. Fuzzy LDA enhancement model is used to enhance the contrast of lane area, another brightness contrast saliency model can be used for robust Lane extraction. Then, two models are applied to lane detection, a two-stage lane detection method is proposed and a blind area vehicle detection method is designed. Firstly, the vehicle area is roughly extracted based on road gray statistics, and then the typical vehicle features are screened finely. Finally, the extracted features and SVM classifiers are used to confirm the candidate regions. Experiments show that: The proposed method can detect the vehicle in the blind area very well and is insensitive to the shape distortion and size change of the vehicle.

scholarly journals Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1021
Author(s):  
Teck Kai Chan ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Intelligent Vehicles ◽  
Smart Device ◽  
Enabling Technology ◽  
Research Gaps ◽  
The Road ◽  
Driving Behaviors ◽  
On The Road

With the concept of Internet-of-Things, autonomous vehicles can provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks. This paper first covers enabling technology involving a vehicle moving out of parking, traveling on the road, and parking at the destination. The development of autonomous vehicles relies on the data collected for deployment in actual road conditions. Research gaps and recommendations for autonomous intelligent vehicles are included. For example, a sudden obstacle while the autonomous vehicle executes the parking trajectory on the road is discussed. Several aspects of social problems, such as the liability of an accident affecting the autonomous vehicle, are described. A smart device to detect abnormal driving behaviors to prevent possible accidents is briefly discussed.

Risk Attitudes and Behavior Among Norwegian Adolescents

2005 ◽  
Vol 10 (1) ◽  
pp. 25-38 ◽  
Author(s):  
Hilde Iversen ◽  
Torbjørn Rundmo ◽  
Hroar Klempe
Keyword(s):  
Public Health ◽  
Traffic Safety ◽  
Behavior Modification ◽  
Health Programs ◽  
Risk Attitudes ◽  
Community Based ◽  
Public Health Programs ◽  
The Core ◽  
And Behavior ◽  
Attitudes And Behavior

Abstract. The core aim of the present study is to compare the effects of a safety campaign and a behavior modification program on traffic safety. As is the case in community-based health promotion, the present study's approach of the attitude campaign was based on active participation of the group of recipients. One of the reasons why many attitude campaigns conducted previously have failed may be that they have been society-based public health programs. Both the interventions were carried out simultaneously among students aged 18-19 years in two Norwegian high schools (n = 342). At the first high school the intervention was behavior modification, at the second school a community-based attitude campaign was carried out. Baseline and posttest data on attitudes toward traffic safety and self-reported risk behavior were collected. The results showed that there was a significant total effect of the interventions although the effect depended on the type of intervention. There were significant differences in attitude and behavior only in the sample where the attitude campaign was carried out and no significant changes were found in the group of recipients of behavior modification.

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