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The present research aims to understand the safety over the midblock road sections and proposes a safety framework using the conventional Time to Collision (TTC) measure. In the present work, the safety framework underlines a supporting structure connecting the actions of the surrounding vehicles and assesses the collisions changes for a given subject vehicle. The Framework principally checks the likelihood of lateral overlap and the time gap between the subject vehicle and its surrounding vehicles. Later, for the trajectory data development, an automated trajectory data development tool is built with the help of image processing for generating the trajectory data from the study sections. In supporting the developed safety framework, the lateral movement of the vehicles is modeled precisely with the help of deep learning. Further, the conceptualized safety framework is tested with the developed trajectory data sets over the study sections. From the results, it is observed that, in mixed traffic, the collision points are over the entire geometry of the study section. In the case of homogeneous traffic, the collision instincts are clustered toward the median lanes. With the advancement of technology, trajectory data development can be a real-time exercise, and the safety framework can be implemented. By applying the study methodology, the critical spots over the road network can be flagged for better treatment and improve safety over the sections.

Forward Collision Warning (FCW) system constantly measures the relative position of the vehicle ahead and then predicts collisions. This paper proposes a new cost-effective and computationally efficient FCW method that uses a time-of-flight (ToF) camera to measure relevant distances to the front vehicle based on license plate detection. First, a Yolo V7 model is used to detect license plates to identify vehicles in front of the ego vehicle. Second, the distance between the front vehicle and the ego vehicle is determined by analyzing the captured depth map by the time-of-flight camera. In addition, the relative speed of the vehicle can be calculated by the direct distance change between the license plate and the camera between two consecutive frames. With a processing speed of 25-30 frames per second, the proposed FCW system is capable of determining relative distances and speeds within 26 meters in the real-time.

The present study explored the intrinsic event-related potential (ERP) features of the effects of acute psychological stress on the processing of motion-in-depth perception using a dual-task paradigm. After a mental arithmetic task was used to induce acute psychological stress, a collision task was used to evaluate motion-in-depth perception. The error value and average amplitude of late slow waves (SW) were significantly larger for the earlier colliding spheres' than for the later colliding spheres. The P1 peak latency in the left occipital region was significantly shorter than that of the right occipital region in the motion-in-depth perception task. Compared to the control condition, the estimated value of residual time-to-collision and error value were significantly reduced, and the N1 peak amplitude and the SW averaged amplitude were significantly increased in the stress condition. Longer motion-in-depth time improved discrimination accuracy and decreased the investment of cognitive resources. Acute psychological stress increased behavioral performance and enhanced attention resources on the motion-in-depth perception task together with greater investment of cognitive resources.

We present an analysis of risk levels on multi-lane roads. The aim is to use the crash metrics to understand which direction of the flow mainly influences the safety in traffic flow. In fact, on multi-lane highways interactions among vehicles occur also with lane changing and we show that they strongly affect the level of potential conflicts. In particular, in this study we consider the Time-To-Collision as risk metric and we use the experimental data collected on the A3 German highway., Comment: 6 pages

The focus of this paper is the crash risk assessment of off-ramps in Xi&rsquo, an. The time-to-collision (TTC) is used for the measurement and cross-comparison of the crash risk of each location. Five sites from the urban expressway in Xi&rsquo, an were selected to explore the TTC distribution. An unmanned aerial vehicle and a camera were used to collect traffic flow data for 20 min at each site. The parameters, including speed, deceleration rate, truck percentage, traffic volume, and vehicle trajectories, were extracted from video images. The TTCs were calculated for each vehicle. The Gaussian mixture model (GMM) was proposed to predict the TTC probability density functions (PDFs) and cumulative density functions (CDFs) for five sites. The Kolmogorov&ndash, Smirnov (K-S) test indicated that the samples followed the estimated GMM distribution. The relationship between the crash risk level and influencing factors was studied by an ordinal logistic regression model and a naive Bayesian model. The results showed that the naive Bayesian model had an accuracy of 86.71%, while the ordinal logistic regression model had an accuracy of 84.81%. The naive Bayesian model outperformed the ordinal logistic regression model, and it could be applied to the real-time collision warning system.

As vehicles get increasingly automated, they need to properly evaluate different situations and assess threats at run-time. In this scenario automated vehicles should be able to evaluate risks regarding a dynamic environment in order to take proper decisions and modulate their driving behavior accordingly. In order to avoid collisions, in this work we propose a risk estimator based on fuzzy logic which accounts for risk indicators regarding (1) the state of the driver, (2) the behavior of other vehicles and (3) the weather conditions. A scenario with two vehicles in a car-following situation was analyzed, where the main concern is to avoid rear-end collisions. The goal of the presented approach is to effectively estimate critical states and properly assess risk, based on the indicators chosen. This work was supported by the AMASS project (H2020- ECSEL) with grant agreement number 692474.

Analyzing a crash using driving recorder data makes it possible to objectively examine factors contributing to the occurrence of the crash. In this study, car-to-cyclist crashes and near crashes recorded on cars equipped with advanced driving recorders were compared with each other in order to examine the factors that differentiate near crashes from crashes, as well as identify the causes of the crashes. Focusing on cases where the car and cyclist approached each other perpendicularly, the differences in the car's and cyclist's parameters such as velocity, distance and avoidance behavior were analyzed. The results show that car-to-cyclist crashes would not be avoidable when the car approaching the cyclist enters an area where the average deceleration required to stop the car is more than 0.45 G (4.4 m/s2). In order for this situation to occur, there are two types of cyclist crash scenarios. In the first scenario, the delay in the drivers’ reaction in activating the brakes is the main factor responsible for the crash. In this scenario, time-to-collision when the cyclist first appears in the video is more than 2.0 s. In the second scenario, the sudden appearance of a cyclist from behind an obstacle on the street is the factor responsible for the crash. In this case, the time-to-collision is less than 1.2 s, and the crash cannot be avoided even if the driver exhibited avoidance maneuvers.

Time-to-collision (TTC) underestimation has been interpreted as an adaptive response that allows observers to have more time to engage in a defensive behaviour. This bias seems, therefore, strongly linked to action preparation. There is evidence that the observer’s physical fitness modulates the underestimation effect so that people who need more time to react (i.e. those with less physical fitness) show a stronger underestimation effect. Here we investigated whether this bias is influenced by the momentary action capability of the observers. In the first experiment, participants estimated the time-to-collision of threatening or non-threatening stimuli while being mildly immobilized (with a chin rest) or while standing freely. Having reduced the possibility of movement led participants to show more underestimation of the approaching stimuli. However, this effect was not stronger for threatening relative to non-threatening stimuli. The effect of the action capability found in the first experiment could be interpreted as an expansion of peripersonal space (PPS). In the second experiment, we thus investigated the generality of this effect using an established paradigm to measure the size of peripersonal space. Participants bisected lines from different distances while in the chin rest or standing freely. The results replicated the classic left-to-right gradient in lateral spatial attention with increasing viewing distance, but no effect of immobilization was found. The manipulation of the momentary action capability of the observers influenced the participants’ performance in the TTC task but not in the line bisection task. These results are discussed in relation to the different functions of PPS.

With regard to impending object–object collisions, observers may use different sources of information to judge time to contact (tC). We introduced changes of the observer’s vantage point to test among three sets of hypotheses: (1) Observers may use a distance-divided-by-velocity algorithm or, alternatively, elaborated τ-formulae, all of which give exact tC information; (2) observers may use simple τ-formulae (i.e., formulae of the type: visual angle divided by its own first temporal derivative); (3) observers may capitalize on non-τ variables. Hypotheses (2) and (3) imply specific patterns of errors. We presented animated, impending collisions between a moving object and a stationary pole to naïve observers. The moving object either was a square tile or a small dot of fixed size. Participants viewed these events in a prediction-motion paradigm from different vantage points, covering a full circle around the setting. As accuracy of responses varied sinusoidally with viewing angle, irrespective of the type of object used, we conclude that observers mainly responded to the perspective view of the gap between object and pole, and less to the object’s changing visual angle, or τ. Results are discussed with regard to evolutionary demands and issues of generalization.

Collision avoidance system (CAS), with the help of surrogate safety measures is a beneficial tool for reducing driver errors and preventing rear-end collisions. One of the most well-known surrogate safety measures to detect rear-end conflicts is Time-to-collision (TTC). TTC refers to the time remaining before the rear-end accident if the course and the speed of vehicles are maintained constant. Different surrogate measures have been derived from TTC; however, the most important are Time Exposed Time-to-collision (TET) and Time Integrated Time-to-collision (TIT). In this paper a new surrogate safety measure based on TTC notion has been developed. This new indicator merges TET and TIT into one measure and gives a score between 0 and 100%, as the probability of collision. Applying this indicator in CAS as a safety measure will be more useful than TET&TIT, to reduce driver errors and rear-end collisions.

In order to improve vehicle safety, a interaction phase between primary and secondary safety systems has been defined. These systems use information provided by the primary safety systems to achieve both the primary and the secondary systems' objectives. It is essential to discriminate whether a collision is avoidable or not and to calculate the time available before the crash happens. This paper shows a method that improves on other simplified methods to calculate the time-to-collision (TTC) to provide a more accurate result that could be used in a collision avoidance system. The work reported in this paper has been partly funded by the Spanish Ministry of Science and Innovation (SAMPLER project TRA2010 20225 C03 01 and TRA2010 20225 C03 03; and IVANET project TRA 2010 15645).

Estimation of time-to-arrival for moving objects is critical to obstacle interception and avoidance, as well as to timing actions such as reaching and grasping moving objects. The source of motion information that conveys arrival time varies with the trajectory of the object raising the question of whether multiple context-dependent mechanisms are involved in this computation. To address this question we conducted a series of psychophysical studies to measure observers’ performance on time-to-arrival estimation when object trajectory was specified by angular motion (“gap closure” trajectories in the frontoparallel plane), looming (colliding trajectories, TTC) or both (passage courses, TTP). We measured performance of time-to-arrival judgments in the presence of irrelevant motion, in which a perpendicular motion vector was added to the object trajectory. Data were compared to models of expected performance based on the use of different components of optical information. Our results demonstrate that for gap closure, performance depended only on the angular motion, whereas for TTC and TTP, both angular and looming motion affected performance. This dissociation of inputs suggests that gap closures are mediated by a separate mechanism than that used for the detection of time-to-collision and time-to-passage. We show that existing models of TTC and TTP estimation make systematic errors in predicting subject performance, and suggest that a model which weights motion cues by their relative time-to-arrival provides a better account of performance.

Over the last decades or so, empirical studies of perception, action, learning, and development have revealed that participants vary in what variable they detect and often rely on nonspecifying variables. This casts doubt on the Gibsonian conception of information as specification. It is argued that a recent ecological conception of information has solved important problems, but insufficiently explains what determines the object of perception. Drawing on recent work on developmental systems, we sketch the outlines of an alternative conception of perceptual information. It is argued that perceptual information does not reside in the ambient arrays; rather, perceptual information is a relational property of patterns in the array and perceptual processes. What a pattern in the ambient flow informs about depends on the perceiver who uses it. We explore the implications of this alternative conception of information for the ecological approach to perception and action. (C) 2009 Elsevier B.V. All rights reserved.

Many animals avoid dark, approaching objects seen against a lighter background but show no or weaker reactions to stimuli with inverted contrast. We investigated whether human observers would respond differently to such stimuli in terms of estimated time-to-arrival. We varied luminances of an approaching, light or dark disk and a plain, grey background, and for several conditions, continuously adjusted calibrations so as to keep contrast and/or overall lightness constant. Since no effects were found, we conclude that humans are able to discard luminance and contrast for the task at hand. Generally, however, performance was affected by different, consecutive regimes of feedback: Initially, without feedback, observers responded inconsistently and much too late; they improved after correct feedback, and in a third block of trials with pseudo-random feedback, they responded increasingly early without reverting to the initial level of uncertainty. We discuss our findings with regard to implications for neural mechanisms, put them in the context of evolutionary considerations, and propose continuative animal behavioral studies.

As an object approaches an observer’s eye, tau, defined as the inverse relative expansion rate of the object’s image on the retina (Lee, 1976) approximates time-to-collision (TTC). Many studies have suggested that human observers use TTC information, but evidence for the use of tau remains inconclusive. Here we present two studies that investigated the use of tau in object-motion and observer-motion situations. In Study I, we dissociated several monocular variables that potentially contributed to TTC perception, and found that participants were most sensitive to TTC information when performing a relative TTC estimation task; and less sensitive to non-time variables such as distance-to-collision, speed and object size. Additionally, when we manipulated sources of information to specify conflicting time-of-arrivals, TTC specified by tau was weighted more than TTC derived from distance and speed. Thus, we concluded that even in the presence of monocular depth information, observers strongly utilize tau when estimating TTC. In Study II, observers estimated TTC of a looming target in the presence or absence of background expansion. Results demonstrated that participants overestimated TTC in situations where the surroundings of the target’s contours expanded at a reduced rate. Moreover, simulated self-motion was unnecessary to induce this bias, as results were comparable in situations where this relative expansion was limited to the target’s immediate surroundings. Therefore, we also concluded that a relative tau variable, based on the relative rate of expansion, is utilized whenever expansion beyond the object’s immediate boundaries is less than the target’s absolute rate of expansion. Master of Science (MSc)

Bij het schatten van de tijd tot contact van een naderend voorwerp blijken proefpersonen vooral gebruik te maken van (globale) optic flow. In de schatting worden "directe" en "indirecte" cues gecombineerd.

Bij het schatten van de tijd tot contact van een naderend voorwerp blijken proefpersonen vooral gebruik te maken van (globale) optic flow. In de schatting worden "directe" en "indirecte" cues gecombineerd.

Beschrijving van twee meetmethoden, geïnstrumenteerd voertuig en video-observatie. Overeenkomst tussen analyses van rijgedrag met TLC en TTC suggereert dat tijdmaten een directe rol kunnen spelen bij het beslissen in het verkeer.

Road traffic crash is a problem in every country. According to World Health Organization, in Europe there are about 127,000 people killed and at least some 2.4 million injured each year. Incident detection algorithms to detect incidents on motorways are well developed. Their objective is to detect incidents as soon as possible so that emergency services can get to the scene of the incident as soon as possible to reduce congestion, to provide medical help if needed and to increase safety of the affected area. Some crashes are traffic related and it may be possible to detect the state of the traffic where the risk of a crash is high. Based on this, premise safety indicators have been developed by [Aron et al, 2003], [Hayward, J. C., 1972], [Lee, C. et al, 2006], and [Pande, A. et al, 2006]. The derivation of these safety indicators are data driven and may be able to forecast the potential of a crash in real time. This study aims to assess the driving risks under Swiss road conditions by using safety indicators. Real traffic data from Automatic Traffic Counts installed on motorways and crash data have been collected in Vaud canton, Switzerland. This paper also discusses the applicability of the proposed safety indicators and the issues associated with their use.