scholarly journals B5G Ultrareliable Low Latency Networks for Efficient Secure Autonomous and Smart Internet of Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sultan S. Alshamrani ◽  
Nishant Jha ◽  
Deepak Prashar
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Time Frame ◽  
Small Time ◽  
Low Latency ◽  
Local Data ◽  
Time Duration ◽  
Vehicle Density ◽  
Total Latency ◽  
On The Road

Recently, 5G and beyond 5G (B5G) systems, Ultrareliable Low Latency Network (URLLC) represents the key enabler for a range of modern technologies to support Industry 4.0 applications, such as transportation and healthcare. Real-world implementation of URLLC can help in major transformations in industries like autonomous driving, road safety, and efficient traffic management. Furthermore, URLLC contributes to the objective of fully autonomous cars on the road that can respond to dynamic traffic patterns by collaborating with other vehicles and surrounding environments rather than relying solely on local data. For this, the main necessity is that how information is to be transferred among the vehicles in a very small time frame. This requires information to be transferred among the vehicles reliably in extremely short time duration. In this paper, we have implemented and analyzed the Multiaccess Edge Computing- (MEC-) based architecture for 5G autonomous vehicles based on baseband units (BBU). We have performed Monte Carlo simulations and plotted curves of propagation latency, handling latency, and total latency in terms of vehicle density. We have also plotted the reliability curve to double-check our findings. When the RSU density is constant, the propagation latency is directly proportional to the vehicle density, but when the vehicle density is fixed, the propagation latency is inversely proportional. When RSU density is constant, vehicle density and handling latency are strictly proportional, but when vehicle density is fixed, handling latency becomes inversely proportional. Total latency behaves similarly to propagation latency; that is, it is also directly proportional.

scholarly journals 5G-Enabled Autonomous Driving Demonstration with a V2X Scenario-in-the-Loop Approach

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7344
Author(s):  
Zsolt Szalay ◽  
Dániel Ficzere ◽  
Viktor Tihanyi ◽  
Ferenc Magyar ◽  
Gábor Soós ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Mass Communication ◽  
Real Life ◽  
Autonomous Driving ◽  
Mobile Technologies ◽  
Low Latency ◽  
Vehicular Communication ◽  
Digital Twin ◽  
The Road ◽  
On The Road

Autonomous vehicles are at the forefront of interest due to the expectations of changing transportation for the better. In order to make better decisions on the road, vehicles use information from various sources: their own sensors, messages arriving from surrounding vehicles and objects, as well as from centralized entities—including their own Digital Twin. Certain decisions require the information to arrive with low latency and some of this information (such as video) requires broadband communication. Furthermore, the vehicles can populate an area, so they can represent mass communication endpoints that still need low latency and massive broadband. The mobility of the vehicles obviously requires the complete coverage of the roads with reliable wireless communication technologies fulfilling the previously mentioned needs. The fifth generation of cellular mobile technologies, 5G, addresses these requirements. The current paper presents real-life scenarios—on the M86 highway and the ZalaZONE proving ground in Hungary—for the demonstration of vehicular communication with 5G support, where the cars exchange sensor and control information with each other, their environment, and their Digital Twins. The demonstrations were carried out through the Scenario-in-the-Loop (SciL) methodology, where some of the actionable triggers were not physically present around the vehicles, but sensed or simulated around their Digital Twin. The measurements around the demonstrations aim to reveal the feasibility of the 5G Non-Standalone Architecture for certain communication scenarios, and they mainly aim to reveal the current latency and throughput limitations under real-life conditions.

Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

2015 ◽  
Vol 27 (6) ◽  
pp. 660-670 ◽  
Author(s):  
Udara Eshan Manawadu ◽  
◽  
Masaaki Ishikawa ◽  
Mitsuhiro Kamezaki ◽  
Shigeki Sugano ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Driving Experience ◽  
The Road ◽  
Traffic Conditions ◽  
Passenger Vehicles ◽  
New Type ◽  
On The Road ◽  
Near Future

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

scholarly journals Autonomous Drifting Using Reinforcement Learning

2021 ◽  
Author(s):  
László Orgován ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Road ◽  
Model Free ◽  
On The Road ◽  
Self Driving Cars ◽  
Planning Problems

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

scholarly journals A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 928
Author(s):  
Man Kiat Wong ◽  
Tee Connie ◽  
Michael Kah Ong Goh ◽  
Li Pei Wong ◽  
Pin Shen Teh ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Safe Driving ◽  
Collision Warning ◽  
Smart Transportation ◽  
The Road ◽  
Safety Mechanism ◽  
Visual Approach ◽  
On The Road ◽  
Forward Collision Warning

Background: Autonomous vehicles are important in smart transportation. Although exciting progress has been made, it remains challenging to design a safety mechanism for autonomous vehicles despite uncertainties and obstacles that occur dynamically on the road. Collision detection and avoidance are indispensable for a reliable decision-making module in autonomous driving. Methods: This study presents a robust approach for forward collision warning using vision data for autonomous vehicles on Malaysian public roads. The proposed architecture combines environment perception and lane localization to define a safe driving region for the ego vehicle. If potential risks are detected in the safe driving region, a warning will be triggered. The early warning is important to help avoid rear-end collision. Besides, an adaptive lane localization method that considers geometrical structure of the road is presented to deal with different road types. Results: Precision scores of mean average precision (mAP) 0.5, mAP 0.95 and recall of 0.14, 0.06979 and 0.6356 were found in this study. Conclusions: Experimental results have validated the effectiveness of the proposed approach under different lighting and environmental conditions.

scholarly journals Low Latency, Low Power in A. I. Perception Modules Development and Implementation for Autonomous Driving

2021 ◽  
pp. 5-6
Author(s):  
Rajdip Das
Keyword(s):  
Deep Learning ◽  
Low Power ◽  
Autonomous Vehicles ◽  
Data Exchange ◽  
Autonomous Driving ◽  
Low Latency ◽  
Ultra Low Power ◽  
Self Service ◽  
Novel Processing ◽  
Deep Learning Neural Network

Self-service vehicles can combine data to boost the understanding of that of other cars, and thus improve safety drive and identification performance. However, it is burdensome to share in between autonomous vehicles, because due to the quantity of data generated by different vehicle types of sensors. In the search for ever faster and more efficient computing, researchers and manufacturers are busy exploring novel processing architectures. Among these, neuromorphic engineering, the emulation of brain function inside computer chips are showing particular promise for applications involving deep learning, an increasingly common form of artificial intelligence (AI) that uses neural networks inspired by brains to uncover patterns in large datasets. In this research, we will examine an ultra-low-power protocol related to low-latency data exchange and Deep Learning Neural Network (DLNN) using neuromorphic computing for addressing AI perception issues in autonomous driving.

scholarly journals A Practice for Object Detection Using YOLO Algorithm

2021 ◽  
pp. 268-272
Author(s):  
Dr. Suwarna Gothane
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Grid Cell ◽  
Confidence Score ◽  
Autonomous Driving ◽  
Time Frame ◽  
Input Image ◽  
Small Time ◽  
Bounding Boxes ◽  
Over Time

When we look at images or videos, we can easily locate and identify the objects of our interest within moments. Passing on this intelligence to computers is nothing but object detection - locating the object and identifying it. Object Detection has found its application in a wide variety of domains such as video surveillance, image retrieval systems, autonomous driving vehicles and many more. Various algorithms can be used for object detection but we will be focusing on the YoloV3 algorithm. YOLO stands for "You Only Look Once". The YOLO model is very accurate and allows us to detect the objects present in the frame. YOLO follows a completely different approach. Instead of selecting some regions, it applies a neural network to the entire image to predict bounding boxes and their probabilities. YOLO is a single deep convolutional neural network that splits the input image into a set of grid cells, so unlike image classification or face detection, each grid cell in YOLO algorithm will have an associated vector in the output that tells us if an object exists in that grid cell, the class of that object, the predicted bounding box for that object. The model here is progressive so it learns more over time, increasing its prediction accuracy over time. The way the model works is that it makes many predictions in one frame and decides to use the most accurate prediction, thus discarding the other. The predictions are made randomly, so if the model feels like there is an object in the frame which is of a very small pixel it will take that also into consideration. To make it more precise and clearer, the model simply creates bounding boxes around everything in the frame, it would make predictions for each box and pick the one with the most confidence score. All this is done in a small-time frame, thus showing why this specific model is the best to use in a real time situation.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

Female Voice Agents in Fully Autonomous Vehicles Are Not Only More Likeable and Comfortable, But Also More Competent

2020 ◽  
Vol 64 (1) ◽  
pp. 1033-1037
Author(s):  
Jiayuan Dong ◽  
Emily Lawson ◽  
Jack Olsen ◽  
Myounghoon Jeon
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Preference Ranking ◽  
Uncanny Valley ◽  
Tracking Data ◽  
Effective Solution ◽  
User Perception ◽  
Future Studies ◽  
Underlying Mechanisms ◽  
The Voice

Driving agents can provide an effective solution to improve drivers’ trust in and to manage interactions with autonomous vehicles. Research has focused on voice-agents, while few have explored robot-agents or the comparison between the two. The present study tested two variables - voice gender and agent embodiment, using conversational scripts. Twenty participants experienced autonomous driving using the simulator for four agent conditions and filled out subjective questionnaires for their perception of each agent. Results showed that the participants perceived the voice only female agent as more likeable, more comfortable, and more competent than other conditions. Their final preference ranking also favored this agent over the others. Interestingly, eye-tracking data showed that embodied agents did not add more visual distractions than the voice only agents. The results are discussed with the traditional gender stereotype, uncanny valley, and participants’ gender. This study can contribute to the design of in-vehicle agents in the autonomous vehicles and future studies are planned to further identify the underlying mechanisms of user perception on different agents.

scholarly journals Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3783
Author(s):  
Sumbal Malik ◽  
Manzoor Ahmed Khan ◽  
Hesham El-Sayed
Keyword(s):  
Autonomous Vehicles ◽  
Communication Technologies ◽  
Autonomous Driving ◽  
Use Cases ◽  
Cooperative Driving ◽  
Vehicle To Vehicle ◽  
Future Challenges ◽  
Vehicle To Infrastructure ◽  
High Level ◽  
Intersection Management

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

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