Autonomous Vehicle Multi-Sensors Localization in Unstructured Environment

Alrousan, Qusay; Alzu’bi, Hamzeh; Pfeil, Andrew; Tasky, Tom

doi:10.4271/2020-01-1029

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…In perception, the vehicle applies several onboard sensors to detect, understand, and interpret the surrounding environment, including static and dynamic objects. Localisation and mapping tasks attempt to locate the vehicle globally with respect to world coordinates [17]. Subsequently, path planning exploits the output of the previous two tasks in order to adopt the optimal and safest feasible route for the RIRS to reach its destination while considering all other possible obstacles on the way [18].…”

Section: Discussionmentioning

confidence: 99%

Localisation and Navigation Framework for Autonomous Railway Robotic Inspection and Repair System

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Localisation and Navigation Framework for Autonomous Railway Robotic Inspection and Repair System

et al. 2021

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“…Recently, LiDAR and vision sensors have been adopted to overcome the challenges of localization in urban scenarios, fusing these types of sensors to cope with the limitation of every single device [ 20 ]. In [ 21 ], a cascading Kalman filter and dynamic object removal model using multi-GNSS, INS, Precise Point Positioning (PPP), and vision to improve vehicle navigation performances in urban scenarios is presented.…”

Section: State Of the Artmentioning

confidence: 99%

Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios

Vignarca

Arrigoni

Sabbioni

2023

Sensors

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The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving.

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“…Interpreting spatio-temporal data visualizations is an example of a larger research problem in vehicle telemetry analytics and the development of multi-sensor autonomous vehicles. Research on data visualization techniques for developing multi-sensor vehicles involves both autonomous vehicle operation systems [6] [7] and human analyst systems [8] [9] where one goal is to minimize the visual interference that occurs when working with real-time multi-sensor data. In the context of human analyst systems there are two forms of visual interference, occlusion [10] [11] and loss of context when zooming in to reveal detail [12].…”

Section: Introductionmentioning

confidence: 99%

A Visual Decision-Support System using Fingerprint Matrices applied to Cyclical Spatio-Temporal Data from Motorsports

Street¹,

Hoeber²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Visualizing cyclical spatio-temporal data is an important part of understanding how and why objects move in the context of motorsports, which is critical feedback for drivers to improve their performance. Current methods have problems such as occlusion and loss of context which significantly limit our ability to see and understand vehicle data. Here we demonstrate how the fingerprint matrix method (which is normally used in lexical analysis) can be applied in vehicle motion analysis to overcome these two problems. Compared to traditional methods using traction circle scatterplot displays of acceleration force data from a race car, our prototype design allows decision makers to see individual datapoints in a more concise display. We show that informative but previously-hidden anomalies and patterns become more easily recognized in the data. Our design generalizes to other cyclical spatio-temporal visualization problems involving transportation, medicine, and the natural world.

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Autonomous Vehicle Multi-Sensors Localization in Unstructured Environment

Cited by 9 publications

References 10 publications

Localisation and Navigation Framework for Autonomous Railway Robotic Inspection and Repair System

Localisation and Navigation Framework for Autonomous Railway Robotic Inspection and Repair System

Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios

A Visual Decision-Support System using Fingerprint Matrices applied to Cyclical Spatio-Temporal Data from Motorsports

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