A Survey on Map-Matching Algorithms

Chao, Pingfu; Xu, Yehong; Hua, Wen; Zhou, Xiaofang

doi:10.1007/978-3-030-39469-1_10

Cited by 58 publications

(39 citation statements)

References 22 publications

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“…Therefore, we only need to focus on these spatial data with the representation of latitude/longitude coordinates, such as trajectories. Existing surveys [23,55,56] have surveyed many existing map-matching techniques. For example, Xi et al [55] split the problem into two types: position matching and curve matching.…”

Section: Map-matchingmentioning

confidence: 99%

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A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

196

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Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

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Section: Map-matchingmentioning

confidence: 99%

“…However, this survey is too old to cover many latest work. Similarly, Zheng et al [23] and Chao et al [56] also ignore some existing effective methods.…”

Section: Map-matchingmentioning

confidence: 99%

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

196

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“…The Map-Matching problematic have been studied over two decades, and thus several formalizations have been proposed. In the following, the definition presented inherits from the one presented in [ 8 ].…”

Section: Road Level Localization (Rll)mentioning

confidence: 99%

A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

Laconte

Kasmi

Aufrère

et al. 2021

Sensors

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In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed. The second component consists of estimating the vehicle’s position in its lane. Finally, the third and last one aims at assessing on which lane the vehicle is currently driving. These two last components are mandatory for safe driving as actions such as overtaking a vehicle require precise information about the current localization of the vehicle. In this survey, we introduce a taxonomy of the localization methods for autonomous vehicles in highway scenarios. We present each main component of the localization process, and discuss the advantages and drawbacks of the associated state-of-the-art methods.

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“…M AP matching, or matching a noisy GPS trajectory to roads in a map, is an important problem in autonomous transportation. The reader is referred to Chao et al for a recent survey on map matching algorithms [1]. In 2009, Newson and Krumm proposed a Hidden Markov Model (HMM) for the road-level map matching problem [2].…”

Section: Introductionmentioning

confidence: 99%

Lane-Level Map Matching Based on HMM

Hansson¹,

Korsberg

Maghsood³

et al. 2021

IEEE Trans. Intell. Veh.

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Lane-level map matching is essential for autonomous driving. In this paper, we propose a Hidden Markov Model (HMM) for matching a trajectory of noisy GPS measurements to the road lanes in which the vehicle records its positions. To our knowledge, this is the first time that HMM is used for lanelevel map matching. Apart from GPS values, the model is further assisted by yaw rate data (converted to a lane change indicator signal) and visual cues in the form of the left and right lane marking types (dashed, solid, etc.). Having defined expressions for the HMM emission and transition probabilities, we evaluate our model to demonstrate that it achieves 95.1% recall and 3.3% median path length error for motorway trajectories.

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A Survey on Map-Matching Algorithms

Cited by 58 publications

References 22 publications

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

Lane-Level Map Matching Based on HMM

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