Video-based personalized traffic learning

Chao, Qianwen; Shen, Jingjing; Jin, Xiaogang

doi:10.1016/j.gmod.2013.07.003

Cited by 36 publications

(24 citation statements)

References 21 publications

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“…Chao et al . [CSJ13] presented a video‐based approach to learn the specific driving characteristics of drivers from videos for traffic animation. This approach formulates the estimation of each vehicle's unique driving habit as a problem of finding the optimal parameter set of a microscopic driving model, which can be solved using an adaptive genetic algorithm.…”

Section: Data‐driven Traffic Simulationmentioning

confidence: 99%

“…Instead of reconstructing virtual traffic based on data acquired from in-road sensors or synthesizing new traffic flows from existing trajectory data, researchers have also employed machine learning algorithms to learn the detailed motion characteristics of vehicles, including acceleration/deceleration in longitudinal direction, and lane-changing process. Chao et al [CSJ13] presented a video-based approach to learn the specific driving characteristics of drivers from videos for traffic animation. This approach formulates the estimation of each vehicle's unique driving habit as a problem of finding the optimal parameter set of a microscopic driving model, which can be solved using an adaptive genetic algorithm.…”

Section: Figure 12: Illustration Of the Pipeline Of The Data-driven Lmentioning

confidence: 99%

“…This phenomenon gives rise to data‐driven traffic animation techniques. Example works include the reconstruction of traffic flows from spatio‐temporal data acquired by existing in‐road sensors [SVDBLM11, WSL13, LWL17], the synthesis of new traffic flows from limited trajectory samples [CDR*18] and the generation of traffic flows through learning behaviour patterns and individual characteristics from traffic monitoring data sets [CSJ13, BMWD16].…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Chao

et al. 2019

Computer Graphics Forum

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100

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Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.

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Section: Data‐driven Traffic Simulationmentioning

confidence: 99%

Section: Figure 12: Illustration Of the Pipeline Of The Data-driven Lmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Chao

et al. 2019

Computer Graphics Forum

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100

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“…However, the method consumes heavy transmission load when too many traffic videos are displayed at a time. Another approach to achieve the aim is traffic simulation [3][4][5][6][7][8][9][10]. The methods first estimate the full traffic state based on the sparse VD sensing data, followed by simulating the dynamics of all individual vehicles to create animations.…”

Section: Related Workmentioning

confidence: 99%

Traffic situation visualization based on video composition

Hsieh

Wang

2016

Computers & Graphics

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a b s t r a c tVehicle detectors (VDs) are usually distributed in a road network to detect macroscopic traffic situations. These detectors provide global information such as vehicle flows, average speed, and road occupancy. Given that the collected statistic data are difficult for citizens to interpret, we visualize the data by providing users with realistic traffic videos. To achieve this aim, our system collects the surveillance videos and VD data that represent the traffic situation of a position. It then builds the connection between these two types of data. Considering the distribution of VDs is much denser than that of surveillance cameras, for those road segments with a VD but without a surveillance camera, one can utilize our system to synthesize videos for visually depicting the traffic situations over there. That is, we estimate vehicle flows from a video and apply the regression model to build the mapping between the flows and VD data. After that, given by a VD dataset, our system retrieves videos that match the VD data and seamlessly composes them to synthesize a traffic video. The evaluations and the experimental results demonstrate the feasibility of our system.

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“…For traffic control, a video-based approach is presented to learn the specific driving characteristics of drivers from the traffic videos [37]. For traffic flow estimation, a virtual loop method is employed to improve the quality of vehicle counting [38].…”

Section: Review Of Related Workmentioning

confidence: 99%