Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

Sazara, Cem; Nezafat, Reza; Cetin, Mecit

doi:10.1109/ivs.2017.7995813

Cited by 12 publications

(7 citation statements)

References 20 publications

(19 reference statements)

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“…The collected samples correspond to RGB images and their semantic labels which are used to train a model to retrieve the semantic embedding. The simulator also produces the precise depth maps of the scene rendered using the Unreal engine 1 . For these two weather conditions, we test the sensor fusion model under the following scenarios:…”

Section: Methodsmentioning

confidence: 99%

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Towards Self-Supervised High Level Sensor Fusion

Khan,

Schön,

Wenzel

2019

Preprint

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In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps. A deep neural network architecture is used for mapping the vision and depth information, respectively, to steering commands. This fusion of information from two sensor sources allows to provide redundancy and fault tolerance in the presence of sensor failures. Even if one of the input sensors fails to produce the correct output, the other functioning sensor would still be able to maneuver the car. Such redundancy is crucial in the critical application of self-driving cars. The experimental results have showed that our method is capable of learning to use the relevant sensor information even when one of the sensors fail without any explicit signal.

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Section: Methodsmentioning

confidence: 99%

“…We consider RGB and Depth camera sensors for training a control module to maneuver a self-driving car. Depth images have several advantages in that they can be used for distance measurement, object detection, and even determination of vehicle trajectories [1]. They can also be used to further improve segmentation [2].…”

Section: Introductionmentioning

confidence: 99%

Towards Self-Supervised High Level Sensor Fusion

Khan,

Schön,

Wenzel

2019

Preprint

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“…Radar sensors present the drawbacks regarding low lateral-resolution data. Even though LiDAR sensors can overcome radar and image sensors’ limitations, they show issues associated with their capability for distinguishing objects, latency identification, and clustering errors [ 246 ]. Accordingly, on-board diagnostics (OBD) require data collected just throughout off-peak hours because of safety concerns.…”

Section: Towards Intelligent Transportation Systemsmentioning

confidence: 99%

A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

Abdulwahid

Mahmoud

Zaidan

et al. 2022

IJERPH

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With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for better road safety and traffic management. As accuracy is needed in modelling, motorcyclist driving behaviour analyses can be performed using sensors that collect driving behaviour characteristics during real-time experiments. This review article systematically investigates the literature on motorcyclist driving behaviour to present many findings related to the issues, problems, challenges, and research gaps that have existed over the last 10 years (2011–2021). A number of digital databases (i.e., IEEE Xplore®, ScienceDirect, Scopus, and Web of Science) were searched and explored to collect reliable peer-reviewed articles. Out of the 2214 collected articles, only 174 articles formed the final set of articles used in the analysis of the motorcyclist research area. The filtration process consisted of two stages that were implemented on the collected articles. Inclusion criteria were the core of the first stage of the filtration process keeping articles only if they were a study or review written in English or were articles that mainly incorporated the driving style of motorcyclists. The second phase of the filtration process is based on more rules for article inclusion. The criteria of inclusion for the second phase of filtration examined the deployment of motorcyclist driver behaviour characterisation procedures using a real-time-based data acquisition system (DAS) or a questionnaire. The final number of articles was divided into three main groups: reviews (7/174), experimental studies (41/174), and social studies-based articles (126/174). This taxonomy of the literature was developed to group the literature into articles with similar types of experimental conditions. Recommendation topics are also presented to enable and enhance the pace of the development in this research area. Research gaps are presented by implementing a substantial analysis of the previously proposed methodologies. The analysis mainly identified the gaps in the development of data acquisition systems, model accuracy, and data types incorporated in the proposed models. Finally, research directions towards ITS are provided by exploring key topics necessary in the advancement of this research area.

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“…In [27], a new method for the recovery of missing LiDAR data points from vehicle trajectory is presented. The method uses microscopic traffic flow models.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Missing LiDAR Data for Accurate AGV Localization

et al. 2022

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This article evaluates several machine learning methods to substitute the missing light detection and ranging data for better spatial localization of industrial automated guided vehicles. Decision trees and ensemble of trees using bagging or boosting techniques have been considered. Also, the k-nearest neighbors algorithm was analyzed. Most of the algorithms have been evaluated based on multiple criteria and hyper parameter tuning. The analysis of the results was done in a comparative way, multiple regression evaluation metrics being considered. The experiments have shown that the extreme gradient boosting algorithm provides the best results in terms of performance, but with timing and resource allocation drawbacks. On the other hand, a simple decision tree model seems to give good results if a tradeoff between performance and prediction time must be made. The k-nearest neighbors algorithm is also performing pretty well, especially because we are experimenting in a static environment.INDEX TERMS Automated Guided Vehicle, Digital Twin, LiDAR, Point cloud estimation

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Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

Cited by 12 publications

References 20 publications

Towards Self-Supervised High Level Sensor Fusion

Towards Self-Supervised High Level Sensor Fusion

A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

Estimation of Missing LiDAR Data for Accurate AGV Localization

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