scholarly journals Road Capacity and Throughput for Safe Driving Autonomous Vehicles

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 95779-95792
Author(s):  
Yuan-Ying Wang ◽  
Hung-Yu Wei
Keyword(s):  
Autonomous Vehicles ◽  
Safe Driving ◽  
Road Capacity

scholarly journals An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1788
Author(s):  
Gomatheeshwari Balasekaran ◽  
Selvakumar Jayakumar ◽  
Rocío Pérez de Prado
Keyword(s):  
Execution Time ◽  
Autonomous Vehicles ◽  
Rapid Development ◽  
Single Layer ◽  
Battery Life ◽  
Learning Approaches ◽  
Task Management ◽  
Inference System ◽  
Safe Driving ◽  
Self Driving Cars

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms).Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

scholarly journals CONGESTION EFFECTS OF AUTONOMOUS TAXI FLEETS

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 971-980 ◽  
Author(s):  
Michal Maciejewski ◽  
Joschka Bischoff
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Traffic Volume ◽  
City Centre ◽  
Conventional Vehicle ◽  
Road Capacity ◽  
Private Car ◽  
The One ◽  
The Impact

Fleets of shared Autonomous Vehicles (AVs) could replace private cars by providing a taxi-like service but at a cost similar to driving a private car. On the one hand, large Autonomous Taxi (AT) fleets may result in increased road capacity and lower demand for parking spaces. On the other hand, an increase in vehicle trips is very likely, as travelling becomes more convenient and affordable, and additionally, ATs need to drive unoccupied between requests. This study evaluates the impact of a city-wide introduction of ATs on traffic congestion. The analysis is based on a multi-agent transport simulation (MATSim) of Berlin (Germany) and the neighbouring Brandenburg area. The central focus is on precise simulation of both real-time AT operation and mixed autonomous/conventional vehicle traffic flow. Different ratios of replacing private car trips with AT trips are used to estimate the possible effects at different stages of introducing such services. The obtained results suggest that large fleets operating in cities may have a positive effect on traffic if road capacity increases according to current predictions. ATs will practically eliminate traffic congestion, even in the city centre, despite the increase in traffic volume. However, given no flow capacity improvement, such services cannot be introduced on a large scale, since the induced additional traffic volume will intensify today’s congestion.

Research on Road Capacity in the Scenarios of Autonomous Vehicles in China

2020 ◽  
Author(s):  
Jing Chen ◽  
Jian Li ◽  
Ning Zhang ◽  
Xi Zhuo ◽  
Yuchuan Du
Keyword(s):  
Autonomous Vehicles ◽  
Road Capacity

scholarly journals Road Segmentation using Semantic Segmentation Networks for ADAS

2019 ◽  
Vol 8 (11) ◽  
pp. 1740-1743
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Warning System ◽  
Semantic Segmentation ◽  
Boundary Line ◽  
Segmentation Result ◽  
Safe Driving ◽  
The Road ◽  
Straight Line ◽  
Road Segmentation

In this paper, we propose a method to automatically segment the road area from the input road images to support safe driving of autonomous vehicles. In the proposed method, the semantic segmentation network (SSN) is trained by using the deep learning method and the road area is segmented by utilizing the SSN. The SSN uses the weights initialized from the VGC-16 network to create the SegNet network. In order to fast the learning time and to obtain results, the class is simplified and learned so that it can be divided into two classes as the road area and the non-road area in the trained SegNet CNN network. In order to improve the accuracy of the road segmentation result, the boundary line of the road region with the straight-line component is detected through the Hough transform and the result is shown by dividing the accurate road region by combining with the segmentation result of the SSN. The proposed method can be applied to safe driving support by autonomously driving the autonomous vehicle by automatically classifying the road area during operation and applying it to the road area departure warning system

Hierarchical control strategy towards safe driving of autonomous vehicles

2018 ◽  
Vol 34 (4) ◽  
pp. 2197-2212 ◽  
Author(s):  
Keji Chen ◽  
Bo Yang ◽  
Xiaofei Pei ◽  
Xuexun Guo
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Hierarchical Control ◽  
Safe Driving

scholarly journals Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2032
Author(s):  
Sampo Kuutti ◽  
Richard Bowden ◽  
Saber Fallah
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Model Performance ◽  
Model Parameters ◽  
Safe Driving ◽  
Weak Supervision ◽  
Rule Based ◽  
Learning Agent ◽  
Weakly Supervised

The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in autonomous vehicles. In this paper, we present a reinforcement learning based approach to autonomous vehicle longitudinal control, where the rule-based safety cages provide enhanced safety for the vehicle as well as weak supervision to the reinforcement learning agent. By guiding the agent to meaningful states and actions, this weak supervision improves the convergence during training and enhances the safety of the final trained policy. This rule-based supervisory controller has the further advantage of being fully interpretable, thereby enabling traditional validation and verification approaches to ensure the safety of the vehicle. We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance. Additionally, we show that when the model parameters are constrained or sub-optimal, the safety cages can enable a model to learn a safe driving policy even when the model could not be trained to drive through reinforcement learning alone.

scholarly journals Detection of Road Images Containing a Counterlight Using Multilevel Analysis

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2210
Author(s):  
JongBae Kim
Keyword(s):  
Multilevel Analysis ◽  
Support System ◽  
Autonomous Vehicles ◽  
Processing Time ◽  
Specific Region ◽  
Detection Accuracy ◽  
The Sun ◽  
Safe Driving ◽  
High Brightness ◽  
Specific Location

In this paper, a method for detecting real-time images that include counterlight produced by the sun, is proposed. It involves applying a multistep analysis of the size, location, and distribution of bright areas in the image. In general, images containing counterlight have a symmetrically high brightness value at a specific location spread over an extremely large region. In addition, the distribution and change in brightness in that specific region have a symmetrically large difference compared with other regions. Through a multistep analysis of these symmetrical features, it is determined whether counterlight is included in the image. The proposed method presents a processing time of approximately 0.7 s and a detection accuracy of 88%, suggesting that the approach can be applied to a safe driving support system for autonomous vehicles.

scholarly journals A Review on Fractal Geometry Enhanced Vehicular Communication under 5G Environment

2021 ◽  
Author(s):  
K Raghavendra ◽  
Deepti Kakkar
Keyword(s):  
Fractal Geometry ◽  
Autonomous Vehicles ◽  
Research Area ◽  
Vehicular Communication ◽  
Safe Driving ◽  
Internet Of Vehicles ◽  
The Past ◽  
Fifth Generation ◽  
The Impact ◽  
Day By Day

A lot of technological advancements in the field of vehicular communication has seen in the past decade, which has brought an increasing in a great extent day by day and it’s becoming as a leading research area which provides the scope in terms of safe driving, accident controllability, enhanced security and portability. As we are moving towards the fifth generation, which is showing its dominance on vehicular communication in the next coming years. In this regards the antennas which are used for communication is also an important factor and this paper brings a complete vision over vehicular communication, relevant antennas specified for the latest 5G communication and the paper is concluded with discussion on Internet of vehicles. The impact of Fractal geometry based antennas in the fields of vehicular communication as well as 5G for connected autonomous vehicles are also discussed through this paper.

Roadrunner+: An Autonomous Intersection Management Cooperating with Connected Autonomous Vehicles and Pedestrians with Spillback Considered

2022 ◽  
Vol 6 (1) ◽  
pp. 1-29
Author(s):  
Michael I.-C. Wang ◽  
Charles H.-P. Wen ◽  
H. Jonathan Chao
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
Waiting Times ◽  
Traffic Signals ◽  
Traffic Load ◽  
The Road ◽  
Road Capacity ◽  
Recent Emergence ◽  
Road Crossing ◽  
Intersection Management

The recent emergence of Connected Autonomous Vehicles (CAVs) enables the Autonomous Intersection Management (AIM) system, replacing traffic signals and human driving operations for improved safety and road efficiency. When CAVs approach an intersection, AIM schedules their intersection usage in a collision-free manner while minimizing their waiting times. In practice, however, there are pedestrian road-crossing requests and spillback problems, a blockage caused by the congestion of the downstream intersection when the traffic load exceeds the road capacity. As a result, collisions occur when CAVs ignore pedestrians or are forced to the congested road. In this article, we present a cooperative AIM system, named Roadrunner+ , which simultaneously considers CAVs, pedestrians, and upstream/downstream intersections for spillback handling, collision avoidance, and efficient CAV controls. The performance of Roadrunner+ is evaluated with the SUMO microscopic simulator. Our experimental results show that Roadrunner+ has 15.16% higher throughput than other AIM systems and 102.53% higher throughput than traditional traffic signals. Roadrunner+ also reduces 75.62% traveling delay compared to other AIM systems. Moreover, the results show that CAVs in Roadrunner+ save up to 7.64% in fuel consumption, and all the collisions caused by spillback are prevented in Roadrunner+.

Safe driving envelopes for path tracking in autonomous vehicles

2017 ◽  
Vol 61 ◽  
pp. 307-316 ◽  
Author(s):  
Matthew Brown ◽  
Joseph Funke ◽  
Stephen Erlien ◽  
J. Christian Gerdes
Keyword(s):  
Autonomous Vehicles ◽  
Path Tracking ◽  
Safe Driving
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