Extracting the fundamental diagram from aerial footage

Makrigiorgis, Rafael; Kolios, Panayiotis; Timotheou, Stelios; Theocharides, Theocharis; Panayiotou, Christos G.

doi:10.1109/vtc2020-spring48590.2020.9128534

Cited by 16 publications

(7 citation statements)

References 25 publications

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“…The distribution of the time interval between vehicles entering the danger zone is exponential, with an average equal to 2 s. This traffic rate is comparable to the average arrival rate at a central intersection in a medium size city around peak hour [14]. As suggested by all common cruise control algorithms, vehicles are allowed into the danger zone if it exist at least one feasible acceleration profile that avoids a collision with the preceding vehicle.…”

Section: A Simulation Scenariomentioning

confidence: 98%

Real-time Trajectory Planning for Managing Demand and Safety during the Crossing of an Intersection by Connected Autonomous Vehicles

Vitale

Kolios

Ellinas

2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

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This work investigates the problem of autonomous intersection crossings facilitated by CAVs by presenting an optimization framework that also considers the vehicle's system state uncertainties and intersection demand management. Assuming linear-Gaussian systems, the presented approach, i.e., AVOID-DM, tackles the problem in two phases. First it decides the best arrival moment in time for a vehicle to enter the area surrounding the intersection, i.e., the danger zone. Then, it selects a set of controls for the vehicle that allows the safe traversal of the intersection, even in presence of vehicle location uncertainty. The overall objective is the maximization of the capacity of the intersection and the presented approach improves previous state-of-the-art solutions both in terms of average number of admitted vehicles and of the vehicles' average speed, while also ensuring smoothness in the vehicles' acceleration profiles.

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Section: A Simulation Scenariomentioning

confidence: 98%

Real-time Trajectory Planning for Managing Demand and Safety during the Crossing of an Intersection by Connected Autonomous Vehicles

Vitale

Kolios

Ellinas

2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

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“…The optimization window chosen, T = 56 s, allows for a CAV to traverse the entire danger zone when traveling with an average speed of 8 m/s. Further, the sampling rate δτ = 0.5 s. The inter-arrival times for CAVs entering the danger zone follow the exponential distribution, with a mean equal to 2 s, that corresponds to the average arrival rate of cars at a centrally-located intersection during peak hour within a medium-size city [12]. The CAVs' initial speed (between 0 and 14m/s) and their lane are selected using a uniform distribution.…”

Section: A Simulation Scenariomentioning

confidence: 99%

Periodic and Event-Triggering for Joint Capacity Maximization and Safe Intersection Crossing

Vitale¹,

Kolios²,

Ellinas³

2022

Preprint

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Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an Intersection Manager (IM) controls CAVs approaching an intersection in order to maximize intersection capacity while minimizing the CAVs' gas consumption. Contrary to most of the works in the literature, the CAVs' location uncertainty is accounted for and periodic communication and re-optimization allows for the creation of safe trajectories for the CAVs. To improve scalability for high-traffic intersections, an event-triggering approach is also developed (AVOID-EVENT) that minimizes computational and communication complexity. AVOID-EVENT reduces the number of re-optimizations required by 92.2%, while retaining most of the benefits introduced by AVOID-PERIOD.

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“…Unlike other state-of-the-art datasets, apart from the vehicles' longitudinal and lateral locations, this dataset is enriched with additional features through post-processing. Specifically, using a Kalman filtering technique, vehicles are tracked in the video footage and their velocity is computed by calculating the approximated displacement over the last 25 frames (see [20] for additional details). Furthermore, to account for the data provided by the road infrastructure, traffic light phases are also obtained from the video footage.…”

Section: Performance Evaluation a Data Collection And Pre-processingmentioning

confidence: 99%

Edge Learning of Vehicular Trajectories at Regulated Intersections

Selvaraj

Vitale

Panayiotou

et al. 2021

2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)

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Trajectory prediction is crucial in assisting both human-driven and autonomous vehicles. Most of the existing approaches, however, focus on straight stretches of road and do not address trajectory prediction at intersections. This work aims to fill this gap by proposing a solution that copes with the higher complexity exhibited for the intersection scenario, leveraging the 5G-MEC capabilities. In particular, the reduced latency and edge computational power are exploited to centrally collect and process measurements from both vehicles (e.g., odometry) and road infrastructure (e.g., traffic light phases). Based on such a holistic system view, we develop a Long Short Term Memory (LSTM) recurrent neural network which, as shown through simulations using a real-world dataset, provides high-accuracy trajectory predictions. The encountered challenges and advantages of the presented approach are analyzed in detail, paving the way for a new vehicle trajectory prediction methodology.

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Extracting the fundamental diagram from aerial footage

Cited by 16 publications

References 25 publications

Real-time Trajectory Planning for Managing Demand and Safety during the Crossing of an Intersection by Connected Autonomous Vehicles

Real-time Trajectory Planning for Managing Demand and Safety during the Crossing of an Intersection by Connected Autonomous Vehicles

Periodic and Event-Triggering for Joint Capacity Maximization and Safe Intersection Crossing

Edge Learning of Vehicular Trajectories at Regulated Intersections

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