4,618 research outputs found
No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles
Defined traffic laws must be respected by all vehicles. However, it is
essential to know which behaviors violate the current laws, especially when a
responsibility issue is involved in an accident. This brings challenges of
digitizing human-driver-oriented traffic laws and monitoring vehicles'
behaviors continuously. To address these challenges, this paper aims to
digitize traffic law comprehensively and provide an application for online
monitoring of legal driving behavior for autonomous vehicles. This paper
introduces a layered trigger domain-based traffic law digitization architecture
with digitization-classified discussions and detailed atomic propositions for
online monitoring. The principal laws on a highway and at an intersection are
taken as examples, and the corresponding logic and atomic propositions are
introduced in detail. Finally, the digitized traffic laws are verified on the
Chinese highway and intersection datasets, and defined thresholds are further
discussed according to the driving behaviors in the considered dataset. This
study can help manufacturers and the government in defining specifications and
laws and can also be used as a useful reference in traffic laws compliance
decision-making. Source code is available on
https://github.com/SOTIF-AVLab/DOTL.Comment: 22 pages, 11 figure
Auto Detection of Number Plate of Person without Helmet
Automated Number Plate Recognition organization would greatly enhance the ability of police to detect criminal commotion that involves the use of motor vehicles. Automatic video investigation from traffic surveillance cameras is a fast-emerging field based on workstation vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient administration of traffic without wearing helmet. In recent years, there has been an increased scope for involuntary analysis of traffic activity. It defines video analytics as computer-vision-based supervision algorithms and systems to extract contextual information from video. In traffic circumstancesnumeroussupervise objectives can be continue by the application of computer vision and pattern gratitude techniques, including the recognition of traffic violations (e.g., illegal turns and one-way streets) and the classification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the acknowledgment of number plates, i.e., automatic number plate recognition (ANPR)
Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving
In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers’ takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers’ takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not
Smart driving aids and their effects on driving performance and driver distraction
In-vehicle information systems have been shown to increase driver workload and cause distraction; both of which are causal factors for accidents. This simulator study evaluates the impact that two designs for a smart driving aid, and scenario complexity have on workload, distraction and driving performance. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely effect driver distraction, while having the effect of decreasing mean driving speed in both the simple and complex driving scenarios. Subjective workload was shown to increase with task difficulty, as well as revealing important differences between the two interface designs
Young people and road user behaviour: attitudes, judgements and behaviour
The problem of the disproportionately high accident and
offence rate of young drivers is a major area for concern
in the field of road safety (Cameron, 1982,1983; Jonah,
1986). Research suggests that young drivers have a
propensity to become involved in risk-taking behaviours
and that this may be due to both motivational factors
(Schuman, et al, 1967; MacMillan, 1975; Wilde, 1982;
Jessor, 1987), and the components of risk perception
(Quenault et al, 1968; Quimby and Watts, 1981; Finn and
Bragg, 1986; Mathews and Moran, 1986).
The present study employed two distinct methodologies
(surveys and the relatively novel technique of
interactive video) in order to examine the attitudes,
judgements and behaviours of a sample of young drivers
(17-19 years) and pre-drivers (11-18 years).
The questionnaire surveys and the Interactive Video
Driving Programme (I. V. D. P. ) revealed that distinct
attitudes towards driving are held as early as 11 years of
age, and that there are several attitudinal, judgemental
and behavioural dimensions along which the sexes and/or
the developmental groups within the driver and pre-driver
sample, could be discriminated. These dimensions related
to perceptions of driving offences, risk-taking attitudes
and behaviours, hazard perception and evaluation, and road
environment awareness.
The use of the I. V. D. P. allowed the examination of driving
behaviours and judgements in simulated decision
situations. Results indicated that there were some
differences in the results produced by the two
methodologies. Results tend to suggest that the more
interactive and pictorial modes of information
presentation may be more successful in assisting young
people to develop more accurate mental representations of
the road traffic environment.
The results are discussed in terms of their implications
for the design and implementation of school-based
pre/driver education programmes. Specifically, issues
such as information content and presentation, and the
targeting of information at young people of different
developmental stages are addressed
Review of graph-based hazardous event detection methods for autonomous driving systems
Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges
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