Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz, Angelica; Ibáñez, Juan Antonio Guerrero

doi:10.3390/s22124614

Cited by 20 publications

(13 citation statements)

References 207 publications

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“…The base of the CAVs–VRU interaction is to predict what other users (cyclists, scooters, pedestrians, motorcyclists, and drivers, among others) intend to do next to make a proper movement decision [ 141 ]. CAVs will not only detect objects but also predict the behavior of other users and notify their intention to the rest of the road users [ 142 ]. For example, in [ 143 ], the authors explain that the interaction without non-verbal communication occurs through two stages: (i) Communication of awareness describes the entire process that must be carried out for the CAVs to detect and identify the VRUs.…”

Section: Discussion and Research Directionsmentioning

confidence: 99%

“…(ii) Communication of intent describes the capabilities of the CAVs to notify the VRUs of their next action (stopping or not stopping for the pedestrian). In [ 142 ], the authors consider that a third stage is necessary, called broadcast communication , which describes the different types of communication between the CAVs and the roadside units, infrastructures, VRUs, and other CAVs.…”

Section: Discussion and Research Directionsmentioning

confidence: 99%

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Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Mualla

Gaud

Calvaresi

et al. 2023

Sensors

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Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights’ phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles’ speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow–speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed.

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Section: Discussion and Research Directionsmentioning

confidence: 99%

Section: Discussion and Research Directionsmentioning

confidence: 99%

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Mualla

Gaud

Calvaresi

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…AVs can detect and classify objects in the vicinity and can notify the driver and other road users about the situation with respect to, for example, pedestrian detection state (pedestrians with intention to cross, pedestrians that stop suddenly or start running), detection of a traffic signal, and detection of objects on the road, among others. AVs can take real-time control of certain operations with the aim of avoiding accidents [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

How to Design the eHMI of AVs for Urgent Warning to Other Drivers with Limited Visibility?

Lim

Kwon

2023

Sensors

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The importance of an external interaction interface (eHMI) has grown in recent years. Most eHMI concepts focus on communicating autonomous vehicle (AV)’s yielding intention to pedestrians at a crossing. However, according to previous studies, pedestrians at a crossing rely mainly on the vehicle’s movement information (implicit communication) rather than information from eHMIs (explicit communication). This paper has the purpose of proposing a specific use case in which the eHMI of future AVs could play an indispensable role in the safety of other road users (ORUs). Often VRUs cannot see the traffic flow due to a series of parked or stopped vehicles, which is a frequent cause of fatal traffic collision accidents. Drivers may also not be able to see approaching pedestrians or other cars from the side for the same reason. In this paper, the impact of an eHMI is tested from the perspective of drivers with limited visibility when a jaywalker steps into the road. A combination of colors, shapes, and information levels is presented on an eHMI. We show that our proposed eHMI design, in the deadlock scenario of a jaywalker and a driver who both lack visibility, significantly reduced the reaction time compared to when there was no eHMI. In the experiment, the willingness to stop, varying from 0 to 5, was measured from the driver’s perspective. The results showed that most users felt uncertainty and did not move quickly when seeing the light band color alone. Textual information on the eHMI was significantly more effective in providing an urgent warning of this specific scenario than vertical and horizontal light bands with color without text. In addition, red color, blinking rapidly above 3 Hz, and egocentric messages were also necessary to reduce the PRT(perception response time). By using text-added eHMI (Vertical + Text eHMI), the mean time to achieve a score above 4 for willingness to stop was 2.113 s faster than when there was no eHMI. It was 2.571 s faster than the time until the slider of the participants reached the maximum level for willingness to stop. This is a meaningful amount of difference when considering a PRT of 2.5 s, which is the Korean road design standard. As eHMIs tend to be applied for smarter mobility, it is expected that they will be more effective in preventing accidents if the eHMI is standardized in autonomous driving level 2 to 3 vehicles driven by humans before fully autonomous driving becomes a reality.

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“…However, considering human-AV interactions in designing AV controllers is critical for ensuring the safety of passengers and the surrounding traffic; AVs that behave in a socially compliant manner enable human drivers to comprehend their actions and respond appropriately . Such interactions could be even more pivotal to consider when it comes to vulnerable road users as safety is the top priority of transportation systems [Reyes-Muñoz and Guerrero-Ibáñez, 2022].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Eco-Driving Control Strategy for Autonomous Fuel Cell Hybrid Vehicles in Connected Traffic Environment

Zhang

Wei

Yang

et al. 2023

Preprint

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Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Cited by 20 publications

References 207 publications

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

How to Design the eHMI of AVs for Urgent Warning to Other Drivers with Limited Visibility?

Hierarchical Eco-Driving Control Strategy for Autonomous Fuel Cell Hybrid Vehicles in Connected Traffic Environment

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