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Found 13 papers, 1 papers with code
Improving the Environmental Perception of Autonomous Vehicles using Deep Learning-based Audio Classification
Although visual sensors of an AV, such as camera, lidar, and radar, help to see its surrounding environment, an AV cannot see beyond those sensors line of sight.
Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection
A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers.
A Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
Data from multiple low-cost in-vehicle sensors (i. e., accelerometer, steering angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural network model, which is a long short-term memory (LSTM) network for predicting the location shift, i. e., the distance that an AV travels between two consecutive timestamps.
Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
In this study, a sensor fusion based GNSS spoofing attack detection framework is presented that consists of three concurrent strategies for an autonomous vehicle (AV): (i) prediction of location shift, (ii) detection of turns (left or right), and (iii) recognition of motion state (including standstill state).
Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles
A spoofed attack is difficult to detect as a spoofer (attacker who performs spoofing attack) can mimic the GNSS signal and transmit inaccurate location coordinates to an AV.
Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment
Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure, and traffic management centers.
Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications
The objective of this study is to develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions in order to maintain a high pedestrian detection accuracy.
Vision-based Pedestrian Alert Safety System (PASS) for Signalized Intersections
The contribution of this paper lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds).
Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network
This attack detection model can detect false information with an accuracy, precision and recall of 95%, 95% and 87%, respectively, while satisfying the real-time communication and computational requirements.
Connected Vehicle Application Development Platform (CVDeP) for Edge-centric Cyber-Physical Systems
Connected vehicle (CV) application developers need a development platform to build, test and debug CV applications, such as safety, mobility, and environmental applications, in an edge-centric Cyber-Physical Systems.
Networking and Internet Architecture
Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment
Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure and traffic management centers.
Cryptography and Security
Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards
Vision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment.
Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy
We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined object detection accuracy.
