Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms

Bachute, Mrinal; Subhedar, Javed M.

doi:10.1016/j.mlwa.2021.100164

Cited by 122 publications

(72 citation statements)

References 62 publications

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“…Pedestrian recognition is the use of sensors to detect pedestrians in or around the path of an AV. It incorporates four components: Segmentation, Feature Extraction, Segment categorization, and Track Categorization [20]. Blurry weather conditions limit the preexisting pedestrian detection methods.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

“…In motion control, the Model Predictive Control algorithm is the main algorithm used for lateral motion control. Still, it has limited fault detection, and the uncertainties that do not match given conditions are not eliminated [20]. In psychology research, there is no real-life implementation for non-functional requirements regarding transparency in self-driving cars, and it's not much studied, so the literature search was limited.…”

Section: Challengesmentioning

confidence: 99%

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A Review on Autonomous Vehicles: Progress, Methods and Challenges

et al. 2022

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Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities.

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Section: Pedestrian Detectionmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

A Review on Autonomous Vehicles: Progress, Methods and Challenges

et al. 2022

View full text Add to dashboard Cite

show abstract

“…One of the problems faced by object detection within the autonomous driving model is the high demand for processing large amounts of data, which places high performance requirements on the algorithms [91].…”

Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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“…Through learning the introduced data and improving the algorithms that are embedded in AI-based technologies, a fundamental transformation in the modelling and simulation mindset was reached. There have been various applications of AI used in different industries, such as energy [64][65][66][67][68][69][70], transportation [71], medicine [72][73][74][75], and various other natural sciences [76][77][78]. Furthermore, the use and implementation of traditional modelling methods have been enhanced by collaborating with AI-based machine learning tools [79][80][81].…”

Section: Artificial Intelligence (Ai) and Machine Learning (Ml)mentioning

confidence: 99%

Artificial Intelligence-Based Machine Learning toward the Solution of Climate-Friendly Hydrogen Fuel Cell Electric Vehicles

Peksen¹

2022

Vehicles

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The rapid conversion of conventional powertrain technologies to climate-neutral new energy vehicles requires the ramping of electrification. The popularity of fuel cell electric vehicles with improved fuel economy has raised great attention for many years. Their use of green hydrogen is proposed to be a promising clean way to fill the energy gap and maintain a zero-emission ecosystem. Their complex architecture is influenced by complex multiphysics interactions, driving patterns, and environmental conditions that put a multitude of power requirements and boundary conditions around the vehicle subsystems, including the fuel cell system, the electric motor, battery, and the vehicle itself. Understanding its optimal fuel economy requires a systematic assessment of these interactions. Artificial intelligence-based machine learning methods have been emerging technologies showing great potential for accelerated data analysis and aid in a thorough understanding of complex systems. The present study investigates the fuel economy peaks during an NEDC in fuel cell electric vehicles. An innovative approach combining traditional multiphysics analyses, design of experiments, and machine learning is an effective blend for accelerated data supply and analysis that accurately predicts the fuel consumption peaks in fuel cell electric vehicles. The trained and validated models show very accurate results with less than 1% error.

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Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms

Cited by 122 publications

References 62 publications

A Review on Autonomous Vehicles: Progress, Methods and Challenges

A Review on Autonomous Vehicles: Progress, Methods and Challenges

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Artificial Intelligence-Based Machine Learning toward the Solution of Climate-Friendly Hydrogen Fuel Cell Electric Vehicles

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