Lane-level positioning for in-vehicle navigation and automated vehicle location (AVL) systems

Du, Jing; Masters, Jeff; Barth, Matthew

doi:10.1109/itsc.2004.1398868

Cited by 32 publications

(20 citation statements)

References 11 publications

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“…In the case of a high temporal resolution (typically, a frequency greater than an observation per second), some highly successful methods have been developed for continuous estimation [38], [24], [12]. However, most data collected at large scale today is generated by commercial fleet vehicles.…”

Section: Introductionmentioning

confidence: 99%

The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data

Hunter

Abbeel

Bayen

2014

IEEE Trans. Intell. Transport. Syst.

110

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Abstract-We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.

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Section: Introductionmentioning

confidence: 99%

The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data

Hunter

Abbeel

Bayen

2014

IEEE Trans. Intell. Transport. Syst.

110

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“…Therefore, it is very important to accurately estimate the position and orientation of vehicle. Most of vehicle navigation systems include several measurement units such as GPS, DGPS and MMS [1][2][3][4][5]. Various kinds of the extended Kalman filter(EKF) have been utilized to estimate the position and orientation of the vehicle [2,5].…”

Section: 서 론mentioning

confidence: 99%

Position and Orientation Estimation of a Maneticalluy Guided-Articulated Vehicle

Yun¹,

Kim²,

Min³

et al. 2011

The Transactions of The Korean Institute of Electrical Engineer

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-For automated guidance control of a magnetically guided-all wheel steered vehicle, it is necessary to have information about position and orientation of the vehicle, and deviations from the reference path in real time. The magnet reference system considered here consists of three magnetic sensors mounted on the vehicle and magnetic markers, which are non-equidistantly buried in the road. This paper presents an observer to estimate such position and orientation at the center of gravity of the vehicle. This algorithm is based on the simple kinematic model of vehicle and uses the data of wheel velocity, steering angle, and the discrete measurements of marker positions. Since this algorithm requires the exact values of initial states, we have also proposed an algorithm of determining the initial position and orientation from the 16 successive magnet pole data, which are given by the magnetic measurement system(MMS). The proposed algorithm is capable of continuing to estimate for the case that the magnetic sensor fail to measure up to three successive magnets. It is shown through experimental data that the proposed algorithm works well within permissible error range. 서 론The articulated vehicle is a new type of public transportation to combine the advantage of commuter buses and railroad vehicles. To achieve good tracking performance similar to railroad vehicle, these vehicles have to be equipped with a lateral guidance system. Such a lateral guidance system requires informations about the vehicles position to calculate the tracking errors relative to the path to be followed. In most cases, the absolute position of the vehicle cannot be measured continuously. msec. It turns out that the information necessary for the guidance control should be estimated at the intermediate time when the magnetic sensors are moving from a marker to next marker, so that this is continuously available for guidance control. Furthermore, it is required to estimate the position and orientation without large errors for the case that the MMS fails to detect three markers consecutively.In this paper, we propose an observer of estimating the vehicle position and orientation. The estimator is composed of an integration algorithm based on vehicle kinematic model, EKF, position update algorithm and orientation angle compensation algorithm using the MMS data. The EKF is used to calculate the vehicle velocity and the side-slip angle at a reference point of the vehicle by using wheel encoders and steering angles data. This is the similar method as one in [5]. The MMS update algorithm is used for estimating the absolute position of vehicle and the orientation angle compensation algorithm

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“…However, local map matching, i.e. determining the lateral vehicle pose within the road segment still remains an open challenge (an approach using Differential GPS is described in Du et al (2004)). Current research focuses on video sensor based estimation of the number of lanes and the subsequent estimation of the vehicle ego pose, namely lateral offset and orientation with respect to the road segment.…”

Section: Conclusion and Future Developmentsmentioning

confidence: 99%

Map Matching for Vehicle Guidance

Drummond¹

2006

Innovations in GIS

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Lane-level positioning for in-vehicle navigation and automated vehicle location (AVL) systems

Cited by 32 publications

References 11 publications

The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data

The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data

Position and Orientation Estimation of a Maneticalluy Guided-Articulated Vehicle

Map Matching for Vehicle Guidance

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