The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment

Sun, Rouxian; Zhuang, Xiangling; Wu, Changxu; Zhao, Guozhen; Zhang, Kan

doi:10.1016/j.trf.2015.02.002

Cited by 97 publications

(63 citation statements)

References 13 publications

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“…Sun et al (2015) report a study of pedestrian estimates of the stopping distance of approaching cars. An excerpt of their results is presented in Fig.…”

Section: Perception Of Stopping Distance By Pedestriansmentioning

confidence: 99%

A statistical law in the perception of risks and physical quantities in traffic

Elvik

2015

Accident Analysis & Prevention

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This paper suggests that a universal psychophysical law influences the perception of risks and physical quantities in traffic. This law states that there will be a tendency to overestimate low probabilities or small quantities, while high probabilities or large quantities may be underestimated. Studies of the perception of risk and physical quantities in traffic have found a highly consistent pattern, which shows that: 1. Pedestrians intending to cross the road overestimate the stopping distance of cars travelling at low speed and underestimate the stopping distance of cars travelling at high speed. 2. Car drivers intending to overtake overestimate the distance needed at low speed, but underestimate it at high speed. 3. Car drivers asked to accelerate from standstill to a given speed overshoot the target speed; when asked to slow down to a stated speed, drivers also overshoot the target speed. 4. When asked what speed to choose to save a given amount of time on a trip of given length, drivers overestimate target speed when initial speed is low and underestimate it when initial speed is high. 5. Drivers overestimate the increase in risk associated with a small increase in speed and underestimate the increase in risk associated with a larger increase in speed. 6. Drivers overestimate the risk of apprehension for traffic offences when it is low and underestimate it when it is high. 7. Road users overestimate the risk associated with comparatively safe modes of tr The paper gives examples of all these misperceptions of physical quantities and risk.

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“…Sun et al (2015) report a study of pedestrian estimates of the stopping distance of approaching cars. An excerpt of their results is presented in Fig.…”

Section: Perception Of Stopping Distance By Pedestriansmentioning

confidence: 99%

A statistical law in the perception of risks and physical quantities in traffic

Elvik

2015

Accident Analysis & Prevention

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“…How pedestrians and vehicles interact is, however, still not fully understood. It is known that these interactions are complex and often affected by various factors ( Figure 1 ) such as: vehicle speed and time to collision (Várhelyi, 1998; Sun et al, 2015; Schneemann and Gohl, 2016), traffic density and size of the gap between the vehicles (Wang et al, 2010), road features such as geometry and signs (Knoblauch et al, 1996), weather and light conditions (Sun et al, 2015), crossing speed (Knoblauch et al, 1996), presence and behavior of other road users (Rosenbloom, 2009; Zhou et al, 2009), demographics of drivers and pedestrians (Oxley et al, 2005; Lobjois and Cavallo, 2007; Tom and Granié, 2011), as well as their experiences, knowledge, motivations, and cognitive state (Mwakalonge et al, 2015). In addition, expectations and feelings of safety or insecurity affect the way the interactions develop (Zhou et al, 2009; Malmsten Lundgren et al, 2017; Sucha et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Communicating Intent of Automated Vehicles to Pedestrians

et al. 2018

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While traffic signals, signs, and road markings provide explicit guidelines for those operating in and around the roadways, some decisions, such as determinations of “who will go first,” are made by implicit negotiations between road users. In such situations, pedestrians are today often dependent on cues in drivers’ behavior such as eye contact, postures, and gestures. With the introduction of more automated functions and the transfer of control from the driver to the vehicle, pedestrians cannot rely on such non-verbal cues anymore. To study how the interaction between pedestrians and automated vehicles (AVs) might look like in the future, and how this might be affected if AVs were to communicate their intent to pedestrians, we designed an external vehicle interface called automated vehicle interaction principle (AVIP) that communicates vehicles’ mode and intent to pedestrians. The interaction was explored in two experiments using a Wizard of Oz approach to simulate automated driving. The first experiment was carried out at a zebra crossing and involved nine pedestrians. While it focused mainly on assessing the usability of the interface, it also revealed initial indications related to pedestrians’ emotions and perceived safety when encountering an AV with/without the interface. The second experiment was carried out in a parking lot and involved 24 pedestrians, which enabled a more detailed assessment of pedestrians’ perceived safety when encountering an AV, both with and without the interface. For comparison purposes, these pedestrians also encountered a conventional vehicle. After a short training course, the interface was deemed easy for the pedestrians to interpret. The pedestrians stated that they felt significantly less safe when they encountered the AV without the interface, compared to the conventional vehicle and the AV with the interface. This suggests that the interface could contribute to a positive experience and improved perceived safety in pedestrian encounters with AVs – something that might be important for general acceptance of AVs. As such, this topic should be further investigated in future studies involving a larger sample and more dynamic conditions.

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“…x (1) i x i (2) x i (3) x i (4) x i (5) x i (6) x i (7) À x i (12) x i The dual variables of x (1) i À x (6) i in Zone 2…”

Section: Orsts Based On Machine Learning Methodsmentioning

confidence: 99%

“…2 Presently, a considerable number of pedestrian crossings in China are actualized without signals. Studies 3,4 have shown that non-signal control tends to obstruct traffic flows and deteriorate the safety of pedestrians. In addition, at signalized crossroads with fixed-time traffic signal control, the fluctuation of traffic demand in peak and off-peak periods are often ignored.…”

Section: Introductionmentioning

confidence: 99%

A real-time signal control strategy at an isolated pedestrian crossing based on radar data

Guo

et al. 2019

Advances in Mechanical Engineering

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Fixed-time traffic signal control strategy in an isolated pedestrian crossing tends to reduce traffic capacity and expose vulnerable road users to more danger. To mitigate the negative impact of previous control strategy, this study proposed an optimal real-time signal timing strategy to protect pedestrian crossing and at the same time minimize the system-wide traffic delay. With the application of a wide-area radar data, the features of vehicles, pedestrians, and the passing time of non-motor vehicles and pedestrian were captured considering conflicts and traffic delay. The support vector machine for regression was utilized to hypothesize traffic delay by training. The discrete values of hypothetical passing time will be tested. The minimum value of delay can be recognized and the corresponding hypothetical passing time will be recommended as the green time for crossing. The performance of the proposed ORSTS outperformed the fixed-time traffic signal control strategy in reducing traffic delay by 22.3%.

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The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment

Cited by 97 publications

References 13 publications

A statistical law in the perception of risks and physical quantities in traffic

A statistical law in the perception of risks and physical quantities in traffic

Communicating Intent of Automated Vehicles to Pedestrians

A real-time signal control strategy at an isolated pedestrian crossing based on radar data

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