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Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey
2022
Sensors
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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. ...
There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s22124614
pmid:35746397
pmcid:PMC9229412
fatcat:kfcgpqtlsvff3ks2turfpnfrom
A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians
2021
IEEE transactions on intelligent transportation systems (Print)
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Index Terms-Vulnerable road users, neural networks, reinforcement learning, transfer learning, autonomous driving, intelligent speed adaptation. ...
We consider scenarios where the vehicle interacts with a distracted pedestrian that might cross the road in hard-to-predict ways and propose a neural network mapping the pedestrian's context onto the appropriate ...
For planning, the agent includes a module for predictions of the other road users' intentions. ...
doi:10.1109/tits.2021.3086397
fatcat:s2tqygxgejhp3mvethfpx6xcqe
Application of Artificial Intelligence in Tesla- A Case Study
2021
International journal of applied engineering and management letters
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A neural network is made up of several deep layers that allow for learning. ...
Tesla introduced Autopilot driver capability for its Model S vehicle. ...
Reinforcement learning, in fact, forecasts the actions of Tesla vehicles rather than the activity of other road users observed by Tesla [37] . ...
doi:10.47992/ijaeml.2581.7000.0113
fatcat:llhggkbppvhl5hx4hks64ekhxe
Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities
2022
Mobile Information Systems
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In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. ...
Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. ...
Acknowledgments is research was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R195), Princess Nourah bint Abdulrahman University, Riyadh, Saudi ...
doi:10.1155/2022/7632892
fatcat:7ffu7l77pngirijr2blvtefhym
Application of Artificial Intelligence in Tesla- A Case Study
2021
Zenodo
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A neural network is made up of several deep layers that allow for learning. ...
Tesla introduced Autopilot driver capability for its Model S vehicle. ...
Reinforcement learning, in fact, forecasts the actions of Tesla
vehicles rather than the activity of other road users observed by Tesla [37]. ...
doi:10.5281/zenodo.5775456
fatcat:wywseexcszbmjb5n765ho6pv4u
Behavior Prediction of Traffic Actors for Intelligent Vehicle using Artificial Intelligence Techniques: A Review
2021
IEEE Access
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This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. ...
The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for ...
), Euclidean error. velocity as input serve as best for sensitive Cyclist Dataset trajectory prediction. circumstances to avoid collisions with vulnerable road users (VRUs). ...
doi:10.1109/access.2021.3116303
fatcat:spra4jjme5ezdpjhmn4uyptwva
Autonomous Vehicles and Avoiding the Trolley (Dilemma): Vehicle Perception, Classification, and the Challenges of Framing Decision Ethics
2019
Cybernetics and systems
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This is further supported by considering a context specific ethical framing for each time phase we anticipate regarding emerging autonomous vehicle technology. ...
This article aims to introduce a degree of technological and ethical realism to the framing of autonomous vehicle perception and decisionality. ...
Acknowledgments All authors are members of the Vision Inspired Driver Assistance Systems (VI-DAS) H2020 research consortia. ...
doi:10.1080/01969722.2019.1660541
fatcat:dyfeqegypzdzfiq4y37l2hbmwm
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
[article]
2018
arXiv
pre-print
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models. ...
Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage ...
Maxim Likhachev from Carnegie Mellon University for his invaluable comments that improved the manuscript. ...
arXiv:1811.11277v1
fatcat:ztrxyydtuveijizfn6a2dmt5ui
VRUNet: Multitask learning model for intent prediction of vulnerable road users
2020
IS&T International Symposium on Electronic Imaging Science and Technology
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2020 pre-print arXiv:2007.05397v1 fatcat:4i37fwflfvbk7hxk6v2speboki
Fast track article for IS&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines proceedings. ...
One of the key challenges with path planning for automated driving on urban roads is that the vehicles have to constantly interact with pedestrians, cyclists, scooters etc. generally identified as Vulnerable ...
with other road users. ...
doi:10.2352/issn.2470-1173.2020.16.avm-109
fatcat:imnp5oexene47jyvbwhxhzyr4q
Autonomous Vehicles in 5G and Beyond: A Survey
[article]
2022
arXiv
pre-print
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With the advent of 5G technology and rise of autonomous vehicles (AVs), road safety is going to get more secure with less human errors. ...
It has the capability to provide greater coverage, better access, and best suited for high density networks. ...
deep neural network with extended kalman filter approach is used to predict future positions of the vehicles in 3D. (2) CR-enabled road side units perform ThZ band detection. ...
arXiv:2207.10510v1
fatcat:myzpimlcazgjbkcr5n4e6zefsi
Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications
2021
Security and Communication Networks
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IoV is introduced to enhance road users' experience by reducing road congestion, improving traffic management, and ensuring the road safety. ...
For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. ...
Different vehicle behavior prediction models are developed i.e., intention trajectory, maneuver-based, and interaction-aware models [56] . ese kinds of models are categorized as input representation and ...
doi:10.1155/2021/8868355
fatcat:z3bnxkaydvd5jl36dcgvta6xa4
Machine learning for next‐generation intelligent transportation systems: A survey
2021
Transactions on Emerging Telecommunications Technologies
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autonomous vehicles, to name a few. ...
ITS are expected to be an integral part of urban planning and future smart cities, contributing to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy ...
ACKNOWLEDGMENTS This work was partly funded by Inria, supported by the French ANR "Investments for the Future" Program reference #ANR-11-LABX-0031-01, and UNICAMP, through the FAPESP Grant number #2017 ...
doi:10.1002/ett.4427
fatcat:ckkpq4vh5be3jg5ouhwhxdvbna
Artificial Co-Drivers as a Universal Enabling Technology for Future Intelligent Vehicles and Transportation Systems
2015
IEEE transactions on intelligent transportation systems (Print)
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This position paper introduces the concept of artificial "co-drivers" as an enabling technology for future intelligent transportation systems. ...
Index Terms-Advanced driver assistance systems (ADAS), artificial cognitive systems, emulation theory of cognition, intelligent vehicles, man-machine systems, optimal control (OC). ...
While these methods may use a variety of algorithms (e.g., hidden Markov models, neural networks of various kinds, and Bayesian networks), they belong to a single class of intention inference methods termed ...
doi:10.1109/tits.2014.2330199
fatcat:b7whou64mbclhe6utm7vjptj4q
The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems
[article]
2021
arXiv
pre-print
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Hence, a new acceptance model of RAS is provided, as a framework for requirements to human-centered AI and for implementing trustworthy RAS by design. ...
The trustworthiness of Robots and Autonomous Systems (RAS) has gained a prominent position on many research agendas towards fully autonomous systems. ...
[69] used artificial neural networks to predict faults based on the noise and vibration of robot's joints; Cho et al. ...
arXiv:2105.04408v1
fatcat:jvlx7lkjizgnbcu2t27ndi4l3q
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
[article]
2018
arXiv
pre-print
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Such a task requires communication between autonomous vehicles and other road users in order to resolve various traffic ambiguities. ...
Today, one of the major challenges that autonomous vehicles are facing is the ability to drive in urban environments. ...
Interaction can guarantee the safety of road users, in particular, pedestrians as the most vulnerable traffic participants. ...
arXiv:1802.02522v2
fatcat:nzeq5eleajcktjl32m2kyqu7rq
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