Coordination Between Connected Automated Vehicles and Pedestrians to Improve Traffic Safety and Efficiency at Industrial Sites

Zhang, Meng; Abbas-Turki, Abdeljalil; Mualla, Yazan; Koukam, Abderraf́ıâa; Tu, Xiaowei

doi:10.1109/access.2022.3185734

Cited by 4 publications

(3 citation statements)

References 29 publications

(34 reference statements)

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“…Indeed, the VRU phase can also be launched using new CAVs–VRU interaction techniques based on a human–robot (or agent) interaction [ 140 ]. 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 ].…”

Section: Discussion and Research Directionsmentioning

confidence: 99%

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%

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Mualla

Gaud

Calvaresi

et al. 2023

Sensors

Self Cite

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“…The drawback of these techniques is their lack of adaptability to changes in pedestrian behavior. The DRL methods are more responsive and efficient, using continuous action space-based policies such as Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) [13][14][15][16].…”

Section: V2p Interactionmentioning

confidence: 99%

Application of Hybrid Deep Reinforcement Learning for Managing Connected Cars at Pedestrian Crossings: Challenges and Research Directions

Brunoud,

Lombard,

Gaud

et al. 2024

Future Transportation

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The autonomous vehicle is an innovative field for the application of machine learning algorithms. Controlling an agent designed to drive safely in traffic is very complex as human behavior is difficult to predict. An individual’s actions depend on a large number of factors that cannot be acquired directly by visualization. The size of the vehicle, its vulnerability, its perception of the environment and weather conditions, among others, are all parameters that profoundly modify the actions that the optimized model should take. The agent must therefore have a great capacity for adaptation and anticipation in order to drive while ensuring the safety of users, especially pedestrians, who remain the most vulnerable users on the road. Deep reinforcement learning (DRL), a sub-field that is supported by the community for its real-time learning capability and the long-term temporal aspect of its objectives looks promising for AV control. In a previous article, we were able to show the strong capabilities of a DRL model with a continuous action space to manage the speed of a vehicle when approaching a pedestrian crossing. One of the points that remains to be addressed is the notion of discrete decision-making intrinsically linked to speed control. In this paper, we will present the problems of AV control during a pedestrian crossing, starting with a modelization and a DRL model with hybrid action space adapted to the scalability of a vehicle-to-pedestrian (V2P) encounter. We will also present the difficulties raised by the scalability and the curriculum-based method.

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“…However, they do not explicitly consider pedestrian movements in their formulations. There is another group of studies on the interaction between individual CAVs and pedestrians without considering signal timing optimization (Fricker & Zhang, 2022; M. Zhang et al., 2020, 2022).…”

Section: Introductionmentioning

confidence: 99%

Advancing the white phase mobile traffic control paradigm to consider pedestrians

Niroumand,

Hajibabai,

Hajbabaie

2024

Computer aided Civil Eng

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Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human‐driven vehicles (CHVs). The movements of vehicles are controlled using both traffic lights and mobile CAV controllers during our newly introduced “white phase.” CAVs navigate platoons of CHVs through the intersection when the white phases are active. In addition to optimizing CAV trajectories, the model optimally selects the status of the traffic light signal among white and green indications for vehicular and walk and do‐not‐walk intervals for pedestrian movements. A receding horizon‐based methodology is used to capture the stochastic nature of the problem and to reduce computational complexity. The case study results show the successful operation of fleets consisting of pedestrians, CAVs, and CHVs with various demand levels through isolated intersections. The results also show that increasing the CAV market penetration rate (MPR) can decrease average intersection delay by up to 27%. Moreover, the average pedestrian, CHV, and CAV delays decrease as the CAV MPR increases and reach their minimum values with a fully CAV fleet. In addition, the presence of the white phase can decrease the intersection average delay by up to 14.7%.

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Coordination Between Connected Automated Vehicles and Pedestrians to Improve Traffic Safety and Efficiency at Industrial Sites

Cited by 4 publications

References 29 publications

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Application of Hybrid Deep Reinforcement Learning for Managing Connected Cars at Pedestrian Crossings: Challenges and Research Directions

Advancing the white phase mobile traffic control paradigm to consider pedestrians

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