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Found 9 papers, 0 papers with code
Experimental Validation of Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
To collect data, a vehicle equipped with a GNSS receiver, along with Inertial Measurement Unit (IMU) is used.
Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems
Specifically, an RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s).
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.
Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles
The objective of this study is to develop an innovative audio analytics-based human trafficking detection framework for autonomous vehicles.
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.
A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehicles
We find that the accuracy of the RL model ranges from 99. 99% to 100%, and the recall value is 100%.
An Innovative Attack Modelling and Attack Detection Approach for a Waiting Time-based Adaptive Traffic Signal Controller
Thus, the objectives of this paper are to: (i) develop a "slow poisoning" attack generation strategy for an ATSC, and (ii) develop a prediction-based "slow poisoning" attack detection strategy using a recurrent neural network -- i. e., long short-term memory model.
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.
