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+.

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Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions

International Journal of Social Robotics ◽

10.1007/s12369-020-00717-x ◽

2020 ◽

Author(s):

Fanta Camara ◽

Charles Fox

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Utility Functions ◽

Hyperbolic Function ◽

The Road ◽

Definition Of ◽

Public Datasets ◽

Discrete Theory ◽

And Control ◽

Road Crossing

AbstractUnderstanding pedestrian proxemic utility and trust will help autonomous vehicles to plan and control interactions with pedestrians more safely and efficiently. When pedestrians cross the road in front of human-driven vehicles, the two agents use knowledge of each other’s preferences to negotiate and to determine who will yield to the other. Autonomous vehicles will require similar understandings, but previous work has shown a need for them to be provided in the form of continuous proxemic utility functions, which are not available from previous proxemics studies based on Hall’s discrete zones. To fill this gap, a new Bayesian method to infer continuous pedestrian proxemic utility functions is proposed, and related to a new definition of ‘physical trust requirement’ (PTR) for road-crossing scenarios. The method is validated on simulation data then its parameters are inferred empirically from two public datasets. Results show that pedestrian proxemic utility is best described by a hyperbolic function, and that trust by the pedestrian is required in a discrete ‘trust zone’ which emerges naturally from simple physics. The PTR concept is then shown to be capable of generating and explaining the empirically observed zone sizes of Hall’s discrete theory of proxemics.

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Pedestrians’ understanding of fully autonomous vehicles (FAV) intent to stop

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641465 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1931-1932

Author(s):

Michal Hochman ◽

Tal Oron-Gilad

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Background Color ◽

Urban Road ◽

The Road ◽

Message Type ◽

On The Road ◽

Future Work ◽

Road Crossing ◽

Distance Conditions

This study explored pedestrians’ understanding of Fully Autonomous Vehicle (FAV) intention and what influences their decision to cross. Twenty participants saw fixed simulated urban road crossing scenes with a FAV present on the road. The scenes differed from one another in the FAV’s messages: the external Human-Machine Interfaces (e-HMI) background color, message type and modality, the FAV’s distance from the crossing place, and its size. Eye-tracking data and objective measurements were collected. Results revealed that pedestrians looked at the e-HMI before making their decision; however, they did not always make the decision according to the e-HMIs’ color, instructions (in advice messages), or intention (in status messages). Moreover, when they acted according to the e-HMI proposition, for certain distance conditions, they tended to hesitate before making the decision. Findings suggest that pedestrians’ decision making to cross depends on a combination of the e-HMI implementation and the car distance. Future work should explore the robustness of the findings in dynamic and more complex crossing environments.

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Assessment of pedestrian mobility on road networks in the City Of Kigali

Global Journal of Pure and Applied Sciences ◽

10.4314/gjpas.v26i2.10 ◽

2020 ◽

Vol 26 (2) ◽

pp. 179-190

Author(s):

David Nkurunziza ◽

Rahman Tafahomi

Keyword(s):

Road Networks ◽

Traffic Signals ◽

City Center ◽

The Road ◽

Bus Stop ◽

Road Signs ◽

Physically Challenged ◽

Road Crossing ◽

The City ◽

Alternative Solutions

This paper analyzed and assessed the pedestrians’ mobility issues that are affecting their free movement and safety in the City of Kigali by outlining the major challenges in the City and providing alternative solutions and measures for improving the mobility and safety of pedestrians. The methodology of the paper was designed based on qualitative method with application of structured and unobtrusive. Referring to the paper findings about the mobility challenges of pedestrians within the City of Kigali, it is noted that the mobility of the pedestrians and their safety is still low and typical problems including road crossing viewed as the second challenges about pedestrians mobility, walking along very close to the road networks due to insufficient footpaths, lacking of enough road signs, lacking of information about pedestrian behavior on road networks, and improper functioning of existing traffic signals as the first challenge. The paper found that the majority of road networks in the City of Kigali did not provide walkways, traffics signals designs and availability is very poor and some of them not functioning, zebra crossing facilities were not provided adequately, pedestrians shelter on bus stop are almost absent and ignored, vehicles travelling speed is still high and does not allow pedestrians to move freely, and the mobility of physically challenged people has been forgotten and there is a need of introducing the pedestrian overpass bridges in clouded zones of the city center, Nyabugogo, Kicukiro and Remera-Giporoso areas of the City of Kigali. Keywords: Mobility, Pedestrian Safety, Road Networks, Traffic Signals, Pedestrians.

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Dynamic Vehicular Traffic Load: Definition and Quantification

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/jtde.v7n2.191 ◽

2019 ◽

Vol 7 (2) ◽

pp. 74-91

Author(s):

Gerald Ostermayer ◽

Christian Backfrieder ◽

Manuel Lindorfer

Keyword(s):

Route Optimization ◽

Traffic Load ◽

Optimization Approach ◽

Vehicular Traffic ◽

Vehicular Communication ◽

The Road ◽

Road Capacity ◽

Novel Approach ◽

Traffic Simulations ◽

Over Time

In this paper, we introduce a method that quantifies the amount of traffic over time by the help of a cloud calculation service and vehicular communication. Furthermore, the approach is applicable also in vehicular traffic simulations, which are widely used in research to demonstrate the effects of proposed solutions to traffic problems. As unused road segments strongly influence the overall traffic load (i.e. used vs full road capacity), we propose a methodology that dynamically calculates the load over time and considers whether specific parts of the road network are used. We introduce two possibilities to filter out distortion of the created statistics due to variation in usage over time. Our novel approach is both simple but widely configurable to fit individual needs. The approach is proven by simulations and application of the load calculation in combination with an intelligent route optimization approach by comparing the optimization gain with the calculated traffic load.

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Tinjauan Aplikasi Piranti Rel Penyeberang Orang (Piren) Berdasarkan Observasi Dan Persepsi Penyeberang Jalan

Warta Penelitian Perhubungan ◽

10.25104/warlit.v22i7.1123 ◽

2010 ◽

Vol 22 (7) ◽

pp. 764-774

Author(s):

Prima Juanita Romadhona

Keyword(s):

Traffic Flow ◽

Waiting Times ◽

Research Approach ◽

The Road ◽

Road Crossing

Densihj of traffic flow in lnrge dties in Indonesin at present has become a big issue; congestion on themain street has become n regulnr tlzing e7.ien1 dmj, especinlly dun·ng the peak hours. Tizis situation ledt.o the pedestrian difficult to cross the road, for example in sclzools in the arterial roads and collectorstreets, pedestrian activihJ sometimes walked chaotic and often 1iehicles do not comply with signs inpedestrian areas so that many acddent cases. Tiiis stud!.; tried t.o review the condition of road userbehavior with the implementation of the Pedestrian Rail Tool (PIREN) which is a tool that thepedestrian crossing is designed to prmnde convenience and safehJ for the pedestrian so it can reducethe number of mctims of acddents while crossing the street. Tire research approach is biJ observationof pedestrian road crossing speeds and long waiting times witlwut and 11rith using PIREN andreaction from pedestrian about PIREN l1J7Plicntlo11 7l'ith the help of questionnaire and then analyzedbij using Importance Perfommnce Analysis metlwd.From 73 PIREN users, appreciating that PIREN can be ne7.U tech tools that make more secure andsafe wlren crossing, but there are still some perfimnance fact.ors that need t.o be improved. Based on thecartesius diagram obtained 2 indicators in quadrant A which are tire main priorihJ for improvedservices, 4 indicators are in tire quadrant B which are perfommnce le7.iels should be maintained, 1indicator is in quadrant C, which is an indimtor of low prion·hJ and 3 indicators in quadrant D thatperformance le7.:iels is excessi11e.Keywords: Pedestrian, PIREN, Importance Perfonnance Analysis

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Pedestrians’ Understanding of a Fully Autonomous Vehicle’s Intent to Stop: A Learning Effect Over Time

Frontiers in Psychology ◽

10.3389/fpsyg.2020.585280 ◽

2020 ◽

Vol 11 ◽

Author(s):

Michal Hochman ◽

Yisrael Parmet ◽

Tal Oron-Gilad

Keyword(s):

Autonomous Vehicles ◽

Learning Effect ◽

Presentation Modality ◽

Physical Size ◽

Urban Road ◽

The Road ◽

Message Type ◽

On The Road ◽

Road Crossing ◽

Over Time

This study explored pedestrians’ understanding of Fully Autonomous Vehicles (FAVs) intention to stop and what influences pedestrians’ decision to cross the road over time, i.e., learnability. Twenty participants saw fixed simulated urban road crossing scenes with a single FAV on the road as if they were pedestrians intending to cross. Scenes differed from one another in the FAV’s, distance from the crossing place, its physical size, and external Human-Machine Interfaces (e-HMI) message by background color (red/green), message type (status/advice), and presentation modality (text/symbol). Eye-tracking data and decision measurements were collected. Results revealed that pedestrians tend to look at the e-HMI before making their decision. However, they did not necessarily decide according to the e-HMIs’ color or message type. Moreover, when they complied with the e-HMI proposition, they tended to hesitate before making the decision. Overall, a learning effect over time was observed in all conditions regardless of e- HMI features and crossing context. Findings suggest that pedestrians’ decision making depends on a combination of the e-HMI implementation and the car distance. Moreover, since the learning curve exists in all conditions and has the same proportion, it is critical to design an interaction that would encourage higher probability of compatible decisions from the first phase. However, to extend all these findings, it is necessary to further examine dynamic situations.

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Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sensors ◽

10.3390/s21113783 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3783

Author(s):

Sumbal Malik ◽

Manzoor Ahmed Khan ◽

Hesham El-Sayed

Keyword(s):

Autonomous Vehicles ◽

Communication Technologies ◽

Autonomous Driving ◽

Use Cases ◽

Cooperative Driving ◽

Vehicle To Vehicle ◽

Future Challenges ◽

Vehicle To Infrastructure ◽

High Level ◽

Intersection Management

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽

10.3390/electronics10121402 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1402

Author(s):

Taehee Lee ◽

Yeohwan Yoon ◽

Chanjun Chun ◽

Seungki Ryu

Keyword(s):

Surface Crack ◽

Autonomous Vehicles ◽

Crack Detection ◽

Model Performance ◽

Semantic Segmentation ◽

Road Surface ◽

Image Brightness ◽

The Road ◽

Detection Model ◽

Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

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