The Truck of the Future: Autonomous and Connected Driving at Daimler Trucks

Ballarin, C.; Zeilinger, Martin

doi:10.4271/2017-01-1931

Cited by 4 publications

(1 citation statement)

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“…Liu et al (2019) presented a new platoon technique that maximizes fuel economy during lane changes. Ballarin and Zeilinger (2017) showed how platoon management benefits society and customers by lowering fuel consumption, CO2 emissions, traffic congestion, and road accidents in their study. Peng, Bonsangue, and Xu (2019) suggest a novel timed automata model for safe vehicle platooning.…”

Section: Problem Statementmentioning

confidence: 99%

Performance Evaluation of Feature Extraction Algorithms for Vehicle Shape Classification

Patel¹,

Srinath²

2022

UPjeng

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Vehicle classification is a classic application of automotive image processing that is necessary for a variety of modern vehicle safety and comfort features. While machine learning-based solutions are effective in these fields and are currently employed extensively in various automotive applications. However, the most challenging aspect of automotive image processing with machine learning methods is gathering adequate quality and quantity datasets to develop such applications. Additionally, imbalanced datasets are common in multiclass automotive image processing, as is the case with the current topic of vehicle platoon management. The effectiveness of available handcrafted feature extractors and classifiers employed for vehicle class categorization varies greatly due to the effect of dataset imbalance. This study aims to examine how the performance of four prominent feature extractors alters when used with the imbalance dataset for vehicle shape classification. Also, the use of image augmentation techniques to increase the dataset size for three vehicle classes: car, bus, and truck, has been presented. Further, using the Support Vector Machine (SVM) classifier, experimental analysis was performed using feature extractors such as Histogram of Oriented Gradient (HOG), Scaled Invariant Feature Transform (SIFT), Speeded-Up Robust Feature (SURF), and Haar. Vehicle shape classification, which is an important characteristic in vehicle platoon management, has been evaluated using Receiver Operating Characteristic (ROC) for both the unbalanced dataset and the augmented dataset. The experimental results demonstrate that using the HOG feature extractor performs better when compared to SIFT, SURF, and HAAR feature extractors on the imbalance dataset. After using an image augmentation technique to add images, output performance improved significantly, with HOG output of 95%, SIFT output of 91%, SURF output of 91%, and HAAR output of 96%.

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Section: Problem Statementmentioning

confidence: 99%

Performance Evaluation of Feature Extraction Algorithms for Vehicle Shape Classification

Patel¹,

Srinath²

2022

UPjeng

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Sensors in Autonomous Vehicles: A Survey

Ayala

Mohd

2021

Journal of Autonomous Vehicles and Systems

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Research and technology in autonomous vehicles is beginning to become well recognized among computer scientists and engineers. Autonomous vehicles contain combination of GPS, LIDAR, cameras, RADAR and ultrasonic sensors (which are hardly ever included). These autonomous vehicles use no less than two sensing modalities, and usually have three or more. The goal of this research is to determine which sensor to use depending on the functionality of the autonomous vehicle and analyze the simi- larities and differences of sensor configurations (which may come from different industries too). This study summarizes sensors in four industries: personal vehicles, public transportation, smart farming, and logistics. In addition, the paper includes advantages and disadvantages of how each sensor configuration are helpful by taking into considerations the activity that has to be achieved in the autonomous vehicle. A table of results is incorporated to organize most of the sensors' availability in the market and their advantages and disadvantages. After comparing each sensor configuration, recommendations are going to be proposed for different scenarios in which some types of sensors will be more useful than others.

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Telematics systems in road transport

Zalewski

2020

JPM

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The Truck of the Future: Autonomous and Connected Driving at Daimler Trucks

Cited by 4 publications

References 0 publications

Performance Evaluation of Feature Extraction Algorithms for Vehicle Shape Classification

Performance Evaluation of Feature Extraction Algorithms for Vehicle Shape Classification

Sensors in Autonomous Vehicles: A Survey

Telematics systems in road transport

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