scholarly journals An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7588
Author(s):  
Huigang Chang ◽  
Nianwen Ning
Keyword(s):  
Pattern Recognition ◽  
Autonomous Vehicles ◽  
Recognition Algorithm ◽  
Driving Safety ◽  
Communication Performance ◽  
Internet Of Vehicles ◽  
Driving Mode ◽  
High Dynamics ◽  
The Stability ◽  
Network Pattern

Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.

scholarly journals Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3425
Author(s):  
Huanping Li ◽  
Jian Wang ◽  
Guopeng Bai ◽  
Xiaowei Hu
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Flow Model ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Transformation Model ◽  
Hydrodynamic Theory ◽  
Traffic Flow Model ◽  
Traffic System ◽  
The Stability

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

scholarly journals Driver seat comfort for level 3-4 autonomous vehicles

Work ◽  
2021 ◽  
Vol 68 (s1) ◽  
pp. S111-S118
Author(s):  
Neil J. Mansfield ◽  
Kartikeya Walia ◽  
Aditya Singh
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Case Methods ◽  
Driving Mode ◽  
Full Control ◽  
Seat Comfort ◽  
Autonomous Mode ◽  
Different Types ◽  
Level 3 ◽  
Level 2

BACKGROUND: Autonomous vehicles can be classified on a scale of automation from 0 to 5, where level 0 corresponds to vehicles that have no automation to level 5 where the vehicle is fully autonomous and it is not possible for the human occupant to take control. At level 2, the driver needs to retain attention as they are in control of at least some systems. Level 3-4 vehicles are capable of full control but the human occupant might be required to, or desire to, intervene in some circumstances. This means that there could be extended periods of time where the driver is relaxed, but other periods of time when they need to drive. OBJECTIVE: The seat must therefore be designed to be comfortable in at least two different types of use case. METHODS: This driving simulator study compares the comfort experienced in a seat from a production hybrid vehicle whilst being used in a manual driving mode and in autonomous mode for a range of postures. RESULTS: It highlights how discomfort is worse for cases where the posture is non-optimal for the task. It also investigates the design of head and neckrests to mitigate neck discomfort, and shows that a well-designed neckrest is beneficial for drivers in autonomous mode.

scholarly journals Cybersafety Approach to Cybersecurity Analysis and Mitigation for Mobility-as-a-Service and Internet of Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1220
Author(s):  
Chee Wei Lee ◽  
Stuart Madnick
Keyword(s):  
Autonomous Vehicles ◽  
Process Analysis ◽  
Hazard Analysis ◽  
Urban Mobility ◽  
Internet Of Vehicles ◽  
On Demand ◽  
Software Updates ◽  
Levels Of Automation ◽  
New Generation ◽  
Convergence Of Technologies

Urban mobility is in the midst of a revolution, driven by the convergence of technologies such as artificial intelligence, on-demand ride services, and Internet-connected and self-driving vehicles. Technological advancements often lead to new hazards. Coupled with the increased levels of automation and connectivity in the new generation of autonomous vehicles, cybersecurity is emerging as a key threat affecting these vehicles. Traditional hazard analysis methods treat safety and security in isolation and are limited in their ability to account for interactions among organizational, sociotechnical, human, and technical components. In response to these challenges, the cybersafety method, based on System Theoretic Process Analysis (STPA and STPA-Sec), was developed to meet the growing need to holistically analyze complex sociotechnical systems. We applied cybersafety to coanalyze safety and security hazards, as well as identify mitigation requirements. The results were compared with another promising method known as Combined Harm Analysis of Safety and Security for Information Systems (CHASSIS). Both methods were applied to the Mobility-as-a-Service (MaaS) and Internet of Vehicles (IoV) use cases, focusing on over-the-air software updates feature. Overall, cybersafety identified additional hazards and more effective requirements compared to CHASSIS. In particular, cybersafety demonstrated the ability to identify hazards due to unsafe/unsecure interactions among sociotechnical components. This research also suggested using CHASSIS methods for information lifecycle analysis to complement and generate additional considerations for cybersafety. Finally, results from both methods were backtested against a past cyber hack on a vehicular system, and we found that recommendations from cybersafety were likely to mitigate the risks of the incident.

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

2021 ◽  
pp. 095440702110205
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Coordinated Control ◽  
Steering System ◽  
Front Wheel ◽  
Path Tracking ◽  
Hierarchical Architecture ◽  
Automatic Steering ◽  
The Stability ◽  
Control Layer

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

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