Hidden Markov model and driver path preference for floating car trajectory map matching

Song, Chengbo; Yan, Xuefeng; Stephen, Nkyi; Khan, Arif Ali

doi:10.1049/iet-its.2018.5132

Cited by 16 publications

(11 citation statements)

References 12 publications

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“…Some studies have focused on lowdimensional manifold models to account for a minor subset of trajectories that are possible at intersections (4). Hidden Markov models have also been proposed (5). Recurrent neural network (RNN) has also been widely used, particularly the long short-term memory (LSTM) model, to understand the complex dynamics of vehicle motions (6).…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Vehicle Trajectory Estimation Based on Lane Change Detection using Smartphone Sensors

Islam

Abdel-Aty

2021

Transportation Research Record

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As technology is moving rapidly toward automation and connectivity, it is of paramount importance to predict vehicle trajectories ahead of time. This not only enhances safety but also ensures mobility in a connected and automated environment. Previous studies have shown that, given the previous trajectory, the future trajectory can be estimated. But this method suffers from considerable drawbacks in the case of intersections as it cannot predict turning movements. It also requires advanced sensors that are not readily available in most vehicles. A smartphone device can also be used in such scenarios, bringing partial automation to vehicles without these sensors. This paper presents an integrated method of estimating vehicle trajectories for both general roadway segments and intersections by using a smartphone. A lane change detection system is taken as an indicator of intersection turning movement estimation and corresponding vehicle trajectories are estimated accordingly. The system can achieve high penetration rates and can be used to replicate onboard units. Sensor readings are taken periodically which are first filtered with a low-pass filter to zero out any high-frequency noise and then fed into a machine learning model to detect lane changes. The model can successfully capture lane changes with smartphone data with high accuracy (95%). Finally, vehicle trajectory is estimated using Chebyshev’s polynomial. This type of estimation system can find applications in collision prediction at intersections between a turning vehicle and a pedestrian on a crosswalk.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Vehicle Trajectory Estimation Based on Lane Change Detection using Smartphone Sensors

Islam

Abdel-Aty

2021

Transportation Research Record

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“…Additionally, the eye and gaze data are also used for early detection of a driver's intentions, which is an interesting feature of ADAS. Most schemes developed for prediction of a driver's maneuvering behavior are principally based on the hidden Markov model (HMM) and its variants [209][210][211][212]. These schemes are applied to the data obtained from the driver's gaze sequence [9] and head position [213].…”

Section: Data Processing Algorithmsmentioning

confidence: 99%

Gaze and Eye Tracking: Techniques and Applications in ADAS

Khan

Lee

2019

Sensors

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Tracking drivers’ eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers’ community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver’s eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver’s eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.

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“…Offline HMM map matching algorithms are applied using historical data, batching the whole input trajectory to find the optimal matching path in the road network [23][24][25][26][27][28]. Whole trajectories enable offline algorithms to take account of the relationship between the front and the back points to achieve higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Other studies, e.g., Quick Matching [26], Multistage Matching [27], and SnapNet [29] consider more factors including the speed constraint, road level, and vehicle heading. With regard to transition probability calculation, to consider temporal relationship of different points, some factors such as speed constraint and free-flow travel time are considered in several studies [23,25,28,30,31]. To consider spatial relationship, some factors are included such as the difference between great-circle distance and route distance [24,28,29,31], difference between vehicle's heading change and road segments' heading change [27], and same road priority [29].…”

Section: Introductionmentioning

confidence: 99%

“…With regard to transition probability calculation, to consider temporal relationship of different points, some factors such as speed constraint and free-flow travel time are considered in several studies [23,25,28,30,31]. To consider spatial relationship, some factors are included such as the difference between great-circle distance and route distance [24,28,29,31], difference between vehicle's heading change and road segments' heading change [27], and same road priority [29]. Based on the analysis of advantages of each algorithm, this study is a pioneering endeavour devoted to comprehensively considering various factors in online map matching, i.e., road level, driver's travel preference, vehicle heading, and network topology (same/adjacent road priority).…”

Section: Introductionmentioning

confidence: 99%

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An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

Zhang

2021

Journal of Advanced Transportation

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Map matching is a key preprocess of trajectory data which recently have become a major data source for various transport applications and location-based services. In this paper, an online map matching algorithm based on the second-order hidden Markov model (HMM) is proposed for processing trajectory data in complex urban road networks such as parallel road segments and various road intersections. Several factors such as driver’s travel preference, network topology, road level, and vehicle heading are well considered. An extended Viterbi algorithm and a self-adaptive sliding window mechanism are adopted to solve the map matching problem efficiently. To demonstrate the effectiveness of the proposed algorithm, a case study is carried out using a massive taxi trajectory dataset in Nanjing, China. Case study results show that the accuracy of the proposed algorithm outperforms the baseline algorithm built on the first-order HMM in various testing experiments.

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Hidden Markov model and driver path preference for floating car trajectory map matching

Cited by 16 publications

References 12 publications

Real-Time Vehicle Trajectory Estimation Based on Lane Change Detection using Smartphone Sensors

Real-Time Vehicle Trajectory Estimation Based on Lane Change Detection using Smartphone Sensors

Gaze and Eye Tracking: Techniques and Applications in ADAS

An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

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