Enhancing routing performance for inter-vehicle communication in city environment

2006 ◽  
Author(s):  
Gang Lu ◽  
Gordon Manson ◽  
Demetrios Belis
Keyword(s):  
Vehicle Communication ◽  
City Environment ◽  
Routing Performance

FPGA Based Vehicle to Vehicle Communication in Spartan 3E

2020 ◽  
Vol 8 ◽  
pp. 14-21
Author(s):  
Surya Man Koju ◽  
Nikil Thapa
Keyword(s):  
Signal Processing ◽  
Autonomous Vehicle ◽  
Processing Unit ◽  
Warning Signals ◽  
Vehicle Communication ◽  
Digital Vlsi ◽  
Vehicle To Vehicle ◽  
Vehicle Operation ◽  
Vehicle Technology ◽  
The Voice

This paper presents economic and reconfigurable RF based wireless communication at 2.4 GHz between two vehicles. It implements digital VLSI using two Spartan 3E FPGAs, where one vehicle receives the information of another vehicle and shares its own information to another vehicle. The information includes vehicle’s speed, location, heading and its operation, such as braking status and turning status. It implements autonomous vehicle technology. In this work, FPGA is used as central signal processing unit which is interfaced with two microcontrollers (ATmega328P). Microcontroller-1 is interfaced with compass module, GPS module, DF Player mini and nRF24L01 module. This microcontroller determines the relative position and the relative heading as seen from one vehicle to another. Microcontroller-2 is used to measure the speed of vehicle digitally. The resulting data from these microcontrollers are transmitted separately and serially through UART interface to FPGA. At FPGA, different signal processing such as speed comparison, turn comparison, distance range measurement and vehicle operation processing, are carried out to generate the voice announcement command, warning signals, event signals, and such outputs are utilized to warn drivers about potential accidents and prevent crashes before event happens.

Comment on “DIO Suppression Attack Against Routing in the Internet of Things”

2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Internet Of Things ◽  
Network Topology ◽  
Impact Analysis ◽  
The Internet ◽  
Routing Performance ◽  
Routing Attack ◽  
The Internet Of Things ◽  
Specific Reason

<div>We have thoroughly studied the paper of Perazzo et al., which presents a routing attack named DIO suppression attack with its impact analysis. However, the considered simulation grid of size 20mx20m does not correspond to the results presented in their paper. We believe that the incorrect simulation detail needs to be rectified further for the scientific correctness of the results. In this comment, it is shown that the suppression attack on such small sized network topology does not have any major impact on routing performance, and specific reason is discussed for such behavior.</div>

scholarly journals State Estimation for Cooperative Lateral Vehicle Following Using Vehicle-to-Vehicle Communication

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 651
Author(s):  
Wouter Schinkel ◽  
Tom van der Sande ◽  
Henk Nijmeijer
Keyword(s):  
State Estimation ◽  
Cooperative Control ◽  
The State ◽  
State Estimator ◽  
Estimation Process ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Control Schemes ◽  
Input Signals ◽  
Vehicle To Vehicle Communication

A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to ensure accurate and stable vehicle following behavior. Control schemes for the cooperative control of longitudinal and lateral vehicle dynamics generally require vehicle state information about the lead vehicle, which in some cases cannot be accurately measured. Including vehicle-to-vehicle communication in the state estimation process can provide the required input signals for the practical implementation of cooperative control schemes. This study is focused on demonstrating the benefits of using vehicle-to-vehicle communication in the state estimation of a lead and following vehicle via simulations. The state estimator, which uses a cascaded Kalman filtering process, takes the operating frequencies of different sensors into account in the estimation process. Simulation results of three different driving scenarios demonstrate the benefits of using vehicle-to-vehicle communication as well as the attenuation of measurement noise. Furthermore, in contrast to relying on low frequency measurement data for the input signals of cooperative control schemes, the state estimator provides a state estimate at every sample.

scholarly journals Time for relative velocity optimal time approach in internet of vehicle communication

2020 ◽  
Vol 981 ◽  
pp. 022053
Author(s):  
Sallauddin Mohmmad ◽  
Mohammed Ali Shaik ◽  
Ramesh Dadi ◽  
Syed Nawaz Pasha ◽  
Shabana ◽  
...  
Keyword(s):  
Relative Velocity ◽  
Optimal Time ◽  
Vehicle Communication

scholarly journals Hazard Perception in City and Highway Environments

2019 ◽  
Vol 63 (1) ◽  
pp. 1404-1408
Author(s):  
John Paul Plummer ◽  
Anastasia Diamond ◽  
Alex Chaparro ◽  
Rui Ni
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Signal Detection Theory ◽  
Response Criterion ◽  
Crash Risk ◽  
Hazard Perception ◽  
Driving Experience ◽  
City Environment ◽  
Video Clips ◽  
The City

Hazard perception (HP) is an important aspect of driving performance and is associated with crash risk. In the current study, we investigate the effect of roadway environment (city vs. highway) and expertise on HP. HP was measured using HP clips that evaluated response lag (defined as the time from the participant’s response to the end of the clip) and fuzzy signal detection theory metrics of response criterion and sensitivity. Forty videos were used: 20 from highway environments and 20 from city environments. Forty-eight participants with a range of driving experience as assessed by the years since obtaining a license (less than 1 year to 24 years) completed the study. There were differences between city and highway environments in response lag and response bias; participants responded earlier to the hazards in the highway environment and exhibited a more liberal response bias. Driving experience was significantly correlated to response lag. When the video clips were categorized by environment, driving experience was only significantly correlated with performance for the city environment.

Index Coded PSK Modulation in Vehicle to Vehicle Communication

2021 ◽  
pp. 1-1
Author(s):  
Jesy Pachat ◽  
Nujoom Sageer Karat ◽  
Anjana Ambika Mahesh ◽  
Deepthi P P ◽  
Sundar Rajan
Keyword(s):  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Vehicle To Vehicle Communication
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