Collecting ambient vehicle trajectories from an instrumented probe vehicle

Coifman, Benjamin; Wu, Mo; Redmill, Keith; Thornton, Douglas

doi:10.1016/j.trc.2016.09.001

Cited by 26 publications

(11 citation statements)

References 39 publications

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“…As a result, the shape of the object may be only partially visible or totally invisible. This issue has been reported by a bunch of studies [32]. One effective and simple solution to fixing the occlusion is installing another LiDAR at a different nearby location [33].…”

Section: A Trajectory Extraction Proceduresmentioning

confidence: 95%

Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Sun

et al. 2019

IEEE Access

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Vehicle-pedestrian conflicts have been the major concern for traffic safety. Surrogate safety measures are widely applied for pedestrian safety evaluation. However, how to quickly identify the vehiclepedestrian surrogate safety measures at the individual site is challenging due to the difficulty of obtaining the high-resolution trajectories of road users. This paper presented an effective method to generate the highresolution traffic trajectories from the roadside deployed Light Detection and Ranging (LiDAR) sensor. The vehicle-pedestrian conflicts can then be identified from the trajectories simply using the speed-distance profile (SDP) of the vehicles. The SDP can be used to develop a rule-based method for vehicle-pedestrian identification. The events can be divided into different risk levels based on the spatial distribution of the SDP. The case study shows that the rule-based method can detect vehicle-pedestrian near-crash events effectively. The other indicators, such as widely used time-to-collision (TTC) or deceleration rate to avoid a crash (DRAC), can be also obtained from the SDP. The engineers can also adjust the thresholds in the rule-based method to meet the specific requirements at different sites. The proposed method can be extended to identify vehicle-vehicle conflicts or vehicle-bicycle conflicts in future studies. INDEX TERMS Roadside LiDAR, vehicle-pedestrian conflicts, surrogate safety measures, high-resolution trajectories, pedestrian safety.

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Section: A Trajectory Extraction Proceduresmentioning

confidence: 95%

Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Sun

et al. 2019

IEEE Access

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“…For further traffic flow theory developments, Coifman developed a process for extracting and cleaning empirical microscopic vehicle relationships by tracking ambient vehicles observed from an instrumented probe vehicle traveling through a freeway traffic stream [31]. In Coifman's experiments, the vehicle is equipped with front and rear horizontal scanning LIDAR sensors with near-rage wide angle coverage and a forward-facing radar with far-range narrow angle coverage.…”

Section: A Comparison With Previous Methodsmentioning

confidence: 99%

“…20(a), LIDAR data were projected into a video image plane, so that human users can accurately assess automated tracking errors and distinguish between different vehicle and non-vehicle targets. Coifman estimated that it should take roughly 8 person-hours to do the manual cleaning for each 2 h real-world tour [31]. With binocular cameras which can track an object and measure the distance simultaneously, it seldom needs human experience to figure out whether there is mismatch between objects and the corresponding distance.…”

Section: A Comparison With Previous Methodsmentioning

confidence: 99%

“…Guo et al [30] developed a car-following strategy based on merging prediction of adjacent vehicles from the results of naturalistic on-road experiments in which a millimeter-wave radar was used to acquire the relative velocity and relative distance between the host vehicle and surrounding vehicles. Coifman et al [31] proposed a method to extract empirical microscopic vehicular interactions on freeways from sensors including LIDAR, millimeter wave radar and DGPS. LIDAR sensors and millimeter wave radar sensors are highly accurate in distance measurement but unreliable in object detection.…”

Section: A Related Workmentioning

confidence: 99%

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A Car-Following Data Collecting Method Based on Binocular Stereo Vision

et al. 2020

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This study proposes the design, implementation, and application of a new car-following data collecting method based on binocular stereo vision system. The car-following data consist of car-following distance, velocity, relative velocity and driving trajectory of a following vehicle. Specifically, triangulation principle is used to measure car-following distance. Robust locally weighted regression smoothing method is applied to automatically filter out the outliers caused by disturbance in real-world driving condition. Real-world experiments indicate that binocular cameras with 25mm focal length have advantages in traffic conditions with long car-following distance and high driving speed, and that binocular cameras with12mm focal length are suitable to measure on urban roads, where car-following distance is short and more driving conditions are needed to be observed. In 0-60m distance measuring range, it is verified that the mean absolute percentage error (MAPE) of binocular cameras with 25mm focal length is 3.2% and the MAPE of binocular cameras with 12mm focal length is 6.3%. The data collecting system helps to advance car following models due to its high accuracy and effective cost. INDEX TERMS Binocular stereo vision, car-following, data collecting method, robust locally weighted regression.

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“…We are interested in missing data problem. So far, LIDAR data has been mostly studied for obstacle detection and tracking purposes but there is also rich information useful for traffic flow theory studies [9][10][11]. Given this motivation, we propose a novel method that recovers missing data points in vehicle trajectories.…”

Section: Introductionmentioning

confidence: 99%

Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

Sazara

Nezafat

Cetin

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-LIDAR has become an important part of many autonomous vehicles with its advantages on distance measurement and obstacle detection. LIDAR produces point clouds which have important information about surrounding environment. In this paper, we collected trajectory data on a two lane urban road using a Velodyne VLP-16 Lidar. Due to dynamic nature of data collection and limited range of the sensor, some of these trajectories have missing points or gaps. In this paper, we propose a novel method for recovery of missing vehicle trajectory data points using microscopic traffic flow models. While short gaps (less than 5 seconds) can be recovered with simple linear regression, and longer gaps are recovered with the proposed method that makes use of car following models calibrated by assigning weights to known points based on proximity to the gaps. Newell's, Pipes, IDM and Gipps' car following models are calibrated and tested with the ground truth trajectory data from LIDAR and NGSIM I-80 dataset. Gipps' calibrated model yielded the best result.

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Collecting ambient vehicle trajectories from an instrumented probe vehicle

Cited by 26 publications

References 39 publications

Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

A Car-Following Data Collecting Method Based on Binocular Stereo Vision

Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

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