Roadrunner+: An Autonomous Intersection Management Cooperating with Connected Autonomous Vehicles and Pedestrians with Spillback Considered

The recent emergence of Connected Autonomous Vehicles (CAVs) enables the Autonomous Intersection Management (AIM) system, replacing traffic signals and human driving operations for improved safety and road efficiency. When CAVs approach an intersection, AIM schedules their intersection usage in a collision-free manner while minimizing their waiting times. In practice, however, there are pedestrian road-crossing requests and spillback problems, a blockage caused by the congestion of the downstream intersection when the traffic load exceeds the road capacity. As a result, collisions occur when CAVs ignore pedestrians or are forced to the congested road. In this article, we present a cooperative AIM system, named Roadrunner+ , which simultaneously considers CAVs, pedestrians, and upstream/downstream intersections for spillback handling, collision avoidance, and efficient CAV controls. The performance of Roadrunner+ is evaluated with the SUMO microscopic simulator. Our experimental results show that Roadrunner+ has 15.16% higher throughput than other AIM systems and 102.53% higher throughput than traditional traffic signals. Roadrunner+ also reduces 75.62% traveling delay compared to other AIM systems. Moreover, the results show that CAVs in Roadrunner+ save up to 7.64% in fuel consumption, and all the collisions caused by spillback are prevented in Roadrunner+.

The role of eye movements in perceiving vehicle speed and time-to-arrival at the roadside

To avoid collisions, pedestrians depend on their ability to perceive and interpret the visual motion of other road users. Eye movements influence motion perception, yet pedestrians’ gaze behavior has been little investigated. In the present study, we ask whether observers sample visual information differently when making two types of judgements based on the same virtual road-crossing scenario and to which extent spontaneous gaze behavior affects those judgements. Participants performed in succession a speed and a time-to-arrival two-interval discrimination task on the same simple traffic scenario—a car approaching at a constant speed (varying from 10 to 90 km/h) on a single-lane road. On average, observers were able to discriminate vehicle speeds of around 18 km/h and times-to-arrival of 0.7 s. In both tasks, observers placed their gaze closely towards the center of the vehicle’s front plane while pursuing the vehicle. Other areas of the visual scene were sampled infrequently. No differences were found in the average gaze behavior between the two tasks and a pattern classifier (Support Vector Machine), trained on trial-level gaze patterns, failed to reliably classify the task from the spontaneous eye movements it elicited. Saccadic gaze behavior could predict time-to-arrival discrimination performance, demonstrating the relevance of gaze behavior for perceptual sensitivity in road-crossing.

Pedestrians’ overt attention affects time-to-arrival estimates of oncoming traffic

To navigate safely, pedestrians need to accurately perceive and predict other road users’ motion trajectories. Previous research has shown that the way visual information is sampled affects motion perception. Here we asked how overt attention affects time-to-arrival prediction of oncoming vehicles when viewed from a pedestrian’s point of view in a virtual road-crossing scenario. In three online experiments, we tested time-to-arrival prediction accuracies when observers pursued an approaching vehicle, fixated towards the road-crossing area, fixated towards the road close to the vehicle’s trajectory or were free to view the scene. When the observer-vehicle distance was high, participants displayed a central tendency in their predicted arrival times, indicating that vehicle speed was insufficiently taken into account when estimating its time-to-arrival. This was especially the case when participants fixated towards the road-crossing area, resulting in time-to-arrival overestimation of slow-moving vehicles and underestimation of fast-moving vehicles. The central tendency bias decreased when participants pursued the vehicle or when the eccentricity between the fixation location and the vehicle trajectory was reduced. Our results identify an unfavorable visual sampling strategy as a potential risk factor for pedestrians and suggest that overt attention is best directed towards the direction of the approaching traffic to derive accurate time-to-arrival estimates. To support pedestrian safety, we conclude that the promotion of adequate visual sampling strategies should be considered in both traffic planning and safety training measures.

Novel Methods In Training Autonomous Vehicles For Urban Roads

<div>This thesis presents the development and application of a novel platform to train autonomous vehicles (AV) for urban roads. Interactive and immersive virtual reality (VR) environments are developed for the collection of mobility preference, behaviour, movement, and orientation data. The resulting naturalistic data can be used directly to train AV control systems. This platform is exemplified in an end-to-end case study resulting in a multi-objective braking system which maximizes both pedestrian safety and passenger comfort. It begins with the development of an immersive VR pedestrian road-crossing environment and compilation of a unique, naturalistic dataset. A vehicle agent is then successfully trained against the dataset, learning a multi-objective brake control policy using deep reinforcement learning methods and reducing the negative influence on passenger comfort by half while maintaining safe braking operation. This platform offers the opportunity to simulate complex, human-in-the-loop scenarios AVs will inevitably face and train them for these scenarios.</div>

The Impact on Mobile Phone use on Pedestrian Road Crossing Behaviour

Mobile phone use at pedestrian crossings has been recognized as a growing problem in the field of traffic safety. The objective of the paper is to analyze the impact of mobile phone use at pedestrian crossings considering specific territory. Signalized and unsignalized intersections are observed in the study. Several factors having the impact on unsafe pedestrian crossing behaviour are identified such as: age, location and the type of mobile phone using. The model of unsafe pedestrian behaviour based on displayed mobile phone use while crossing the intersection is constructed. It has been shown in this research that talking and texting on mobile phone distract pedestrians. Listening to music does not affect pedestrians to behave unsafely because it requires less cognitive activity than talking or texting. Also, location affects the pedestrian crossing behavior. The results of this research can serve the purpose of preventing the mobile phones use and reduce the negative impact on pedestrian crossing behavior.

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.