Research on effects and influence by presenting information on priority order and oncoming vehicle to operators for teleoperation of multiple autonomous vehicles

With self-driving vehicles, it is possible to manage multiple vehicles from a remote location even if one observer does not have a driver in the driver’s seat. Therefore, demonstration experiments are being conducted in various places to remotely monitor two autonomous vehicles and operate them as needed. However, when one observer manages multiple vehicles, the amount of information that can be processed is limited. If we can assist with an appropriate amount of information, we may be able to manage more vehicles. In this study, we perform an experiment in which the priority and the type of assist information are changed and presented to the observer in the overtaking scene of a parked vehicle using a simulator. Focusing on the burden on the observer during remote management of multiple units, the purpose is to identify the information required for monitoring and reduce the burden from changes depending on the type of information to be assisted.

Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions

Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency.

Variable-drift diffusion models of pedestrian road-crossing decisions

Human behavior and interaction in road traffic is highly complex, with many open scientifi?c questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency.

Modeling driver behavior in interactions with other road users

Driver models help improve and evaluate systems for road crash mitigation and avoidance. As systems develop and address increasingly complex scenarios. Driver models also need to be developed to be able to account for the interactions among these road users. Even as we improve driver modeling with control-theory models and actual data-driven implementations, existing driver models fail to sufficiently take interaction among road users into consideration. This paper addresses this insufficiency by proposing a new operational framework to computationally model interactions among road users. For this purpose, we introduce a definition for interaction among road users. The modeling framework is demonstrated by a specific driving scenario: the overtaking of a cyclist when an oncoming vehicle may be present. In this scenario, modeling driver interaction using Unified modeling language within our framework can lead to improved crash mitigation and avoidance through tailored system activation of automated emergency braking

The Effects of Restricted Sight Distances on Drivers at Simulated Rural Intersections

Short sight distances at rural intersections can result in reduced safety including drivers choosing smaller gaps between oncoming vehicles (Yan & Richards, 2010). Conversely, increased sight distances may result in improved driver confidence, but not safety as seen at rail crossings (Ward & Wilde, 1996). Thus, simply increasing sight distances may not result in linear gains in safety. Establishing appropriate lower and upper-visibility limits at rural intersections will minimize operational costs for clearing and grubbing labor and may reduce serious injury and fatal crashes by promoting safer crossing behavior around rural thru-STOP intersections. This driving simulation study will examine intersection visibility and other intersection design factors that influence driver behavior at rural thru-STOPs. Time to collision, or TTC, is a primary motivating factor on driver decisions to cross intersections. Drivers’ TTC perception is a function of oncoming vehicle speed, distance, and rate of retinal expansion (tau; Hancock & Manser, 1997). We hypothesize TTC factors may interact with confidence in intersection crossing judgments. Specifically, when people must quickly make judgments, their confidence must be estimated after the decision has been made, but if the time pressure is not high and the decision is not immediate, people’s judgments and their confidence in those judgments can both be considered prior to action. This can be problematic because people are normally overconfident in their judgments (Harvey, 1997). The present study comprised a crossing judgment block, and a mainline drive block. The experimental stimuli were validated by engineers with experience in rural intersections, who rated the simulated intersections as “significantly representative” to real-world intersections through an average rating score of 5.25 ( SD = 0.5) on a 7-pt scale. This was done to ensure that the study findings would be more likely to be applicable to world intersections. The goal of the study is to identify whether sight distance and other variables, including speed for the judgment task and vehicle proximity to the intersection for the mainline drive task, impact safety at rural intersections.

Horizontal and Vertical Curvature and its Effects on Driver Passing Choice

Passing maneuvers on rural two-lane highways require drivers to enter the opposing traffic lane to overtake an impeding vehicle. A successful maneuver requires the driver to correctly judge the time it will take to complete the pass, and the distance to and speed of the oncoming vehicle. Previous studies have shown that the type and speed of impeding vehicle, traffic volume, roadway cross-section, and driver characteristics influence gap-acceptance behavior, but have not considered vertical curvature or specified directionality in the horizontal curvature. This paper describes a driving simulation experiment in which these geometric, situational, and driver characteristic variables were collected for 643 passing attempts. A logistic regression model was developed to infer the effects of horizontal and vertical curvature on driver passing decisions. This study uniquely quantified that drivers were: more likely to pass if the road curved to the left than if it was straight and less likely to pass if the road curved to the right; more likely to pass if there was a sag curve than if the road was flat and less likely to pass if there was a crest curve; and less likely to pass when traveling uphill than if the road was level. As the influence of roadway geometry on gap-acceptance decisions is not currently implemented in overtaking models, the results of this study have practical implications for microsimulation of rural two-lane highways, highway design, and highway safety, and the inclusion of roadway geometry variables may improve future modeling of roadway capacity and passing locations along two-lane highways.

The effects of using passing beams during the day in real traffic conditions

This paper presents a study on the implications for road safety of the use of passing beams during the day. The genesis of the problem and conditions that have led to the use of passing beams by vehicles during the day is presented first. Then, the photometric requirements for passing beams are evaluated in terms of their signal role, comparing them with the requirements for daytime running lights. The main part of this paper presents a report on field tests carried out under real road conditions. The tests were done in order to measure the impact of using passing beams by day on the distance at which the oncoming vehicle could be detected. Also, the correctness of the estimation of relative positions of oncoming vehicles at the same or different distances was examined with different combinations of passing beams on or off in the tested vehicles. The research confirms the effectiveness of using passing beams during the day and the need to harmonize the obligation to use passing beams.