On the instrumentation and classification of autonomous cars

Zanchin, Betina Carol; Adamshuk, Rodrigo; Santos, Max Mauro Dias; Collazos, Kathya Silvia

doi:10.1109/smc.2017.8123022

Cited by 27 publications

(20 citation statements)

References 6 publications

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“…Autonomous cars are supposed to bring many benefits to society. The advantages include increasing mobility, limiting congestion, improving road safety by reducing collisions and accidents, and having a positive impact on the environment [23].…”

Section: Autonomous Cars: Basic Informationmentioning

confidence: 99%

Autonomous vehicles: basic issues

CZECH¹

2018

SJSUT.ST

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The work was dedicated to the subject of innovative autonomous vehicles on the transport market. The paper presents basic information about autonomous cars: a nomenclature characteristic of autonomous vehicles, along with the terms "automatic", "autonomous", "self-drive" and "driverless". The article also presents various types of autonomous cars based on the most popular classifications in the world. The purpose of the work is to present basic issues related to autonomous vehicles.

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Section: Autonomous Cars: Basic Informationmentioning

confidence: 99%

Autonomous vehicles: basic issues

CZECH¹

2018

SJSUT.ST

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“…The same classification is also adopted by other organisations such as the International Organisation of Motor Vehicle Manufacturer (OICA) and the German Federal Highway Research Institute (BASt). The US National Highway Traffic Safety Administration (NHTSA) initially had a different classification, as compared in Table 1 [12]. Since then, NHTSA has also adopted SAE's six levels of automation in the Federal Automated Vehicle Policy.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 3 summarises the above discussed features and functions achieved at each level according to the SAE J3016 standard [12]. Autonomous vehicles have several advantages including improved safety and lower road congestion resulting in lower fuel/energy consumption [4,6].…”

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confidence: 99%

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Ahangar

Ahmed

Khan

et al. 2021

Sensors

220

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The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatalities in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). Therefore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication technologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium-range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency. This research presents a comprehensive study of AV technologies and identifies the main advantages, disadvantages, and challenges.

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“…Autonomous cars do not have such limitation, but, in light of the future industrialization of the product estimated for the first years of the next decade [1], it is becoming evident the importance of reducing the cost of the additional equipment needed by these vehicles. We can achieve this goal in different ways, e.g., by reducing the number of sensors, in particular LIDARs, or by reducing the computational power needed to process data [2].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation on Embedded Systems via Superpixel Convolutional Networks

Mentasti

Matteucci

2019

2019 European Conference on Mobile Robots (ECMR)

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In this paper we describe a lightweight framework for fast image segmentation on embedded systems, based on superpixels, which leverages on convolutional and graphconvolutional neural networks. In particular, we analyzed different superpixel representation looking for the best tradeoff between the efficiency of the system and richness of the description. Similarly, we analyzed different network sizes, balancing the number of filters used and the prediction accuracy. We also compared two different convolutional architecture, one based on the classical encoder-decoder paradigm and one based on graphs, to guarantee a most accurate representation of the image structure. The architecture was tested on the KITTI dataset using an embedded system with CUDA capabilities.

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On the instrumentation and classification of autonomous cars

Cited by 27 publications

References 6 publications

Autonomous vehicles: basic issues

Autonomous vehicles: basic issues

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Image Segmentation on Embedded Systems via Superpixel Convolutional Networks

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