Vehicle Speed Estimation Using Computer Vision and Evolutionary Camera Calibration

Mejia, Hector; Palomo, Esteban J.; López‐Rubio, Ezequiel; Pineda, Israel; Fonseca, Rigoberto

doi:10.52591/202112072

Cited by 2 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Useful information including averaged speed and density of vehicles on a given stretch of road can be obtained using big data analysis and artificial intelligence techniques. Realtime data and insight can be exchanged to city stakeholders through different channels using dedicated application programming interfaces (APIs) [2]. In this context, a variable speed limit (VSL) system can be deployed and integrated with intelligent infrastructures to alleviate traffic congestion and maximize traffic flows in different scenarios [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…The VSL system is designed in a way that vehicles on each lane of the road network can drive at a safe speed in km/h shown below the overhead gantry signboard 1 . It is worth noting that although driving at a lower speed below the VSL is legal, it is usually not recommended as it will easily lead to a bottleneck for subsequent vehicles trying to follow the speed, and thus reducing traffic flow 2 . A large body of work has been found in the literature for an optimal design of advisory speed for different application purposes, see papers [5]- [12] in this direction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention

Wu¹,

Liu²

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Nowadays, intelligent highway traffic network is playing an important role in modern transportation infrastructures. A variable speed limit (VSL) system can be facilitated in the highway traffic network to provide useful and dynamic speed limit information for drivers to travel with enhanced safety. Such system is usually designed with a steady advisory speed in mind so that traffic can move smoothly when drivers follow the speed, rather than speeding up whenever there is a gap and slowing down at congestion. However, little attention has been given to the research of vehicles' behaviours when drivers left the road network governed by a VSL system, which may largely involve unexpected acceleration, deceleration and frequent lane changes, resulting in chaos for the subsequent highway road users. In this paper, we focus on the detection of traffic flow anomaly due to drivers' lane change intention on the highway traffic networks after a VSL system. More specifically, we apply graph modelling on the traffic flow data generated by a popular mobility simulator, SUMO, at road segment levels. We then evaluate the performance of lane changing detection using the proposed Lane-GNN scheme, an attention temporal graph convolutional neural network, and compare its performance with a temporal convolutional neural network (TCNN) as our baseline. Our experimental results show that the proposed Lane-GNN can detect drivers' lane change intention within 90 seconds with an accuracy of 99.42% under certain assumptions. Finally, some interpretation methods are applied to the trained models with a view to further illustrate our findings.

show abstract

Section: Introductionmentioning

confidence: 99%