Anomaly modelling in machine learning based navigation system of autonomous vehicles

2020 ◽  
Author(s):  
Hossein Eshraghi ◽  
Babak Majidi ◽  
Ali Movaghar
Keyword(s):  
Machine Learning ◽  
Navigation System ◽  
Autonomous Vehicles

Night Vision Patrolling Rover Navigation System for Women’s Safety Using Machine Learning

2019 ◽  
Vol 23 (4) ◽  
pp. 1136-1148
Author(s):  
Gopalakrishnan K ◽  
Thiruvenkatasamy S ◽  
Prabhakar E ◽  
Aarthi R
Keyword(s):  
Machine Learning ◽  
Navigation System ◽  
Night Vision

scholarly journals Imitating a Safe Human Driver Behaviour in Roundabouts Through Deep Learning

2020 ◽  
Vol 19 (1) ◽  
pp. 85-88
Author(s):  
A. S. J. Cervera ◽  
F. J. Alonso ◽  
F. S. García ◽  
A. D. Alvarez
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Driver Behaviour ◽  
Safety Issues ◽  
Current State ◽  
Surrounding Environment ◽  
Human Driver ◽  
State Of The Environment

Roundabouts provide safe and fast circulation as well as many environmental advantages, but drivers adopting unsafe behaviours while circulating through them may cause safety issues, provoking accidents. In this paper we propose a way of training an autonomous vehicle in order to behave in a human and safe way when entering a roundabout. By placing a number of cameras in our vehicle and processing their video feeds through a series of algorithms, including Machine Learning, we can build a representation of the state of the surrounding environment. Then, we use another set of Deep Learning algorithms to analyze the data and determine the safest way of circulating through a roundabout given the current state of the environment, including nearby vehicles with their estimated positions, speeds and accelerations. By watching multiple attempts of a human entering a roundabout with both safe and unsafe behaviours, our second set of algorithms can learn to mimic the human’s good attempts and act in the same way as him, which is key to a safe implementation of autonomous vehicles. This work details the series of steps that we took, from building the representation of our environment to acting according to it in order to attain safe entry into single lane roundabouts.

scholarly journals Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Ruiyu Zhou ◽  
Zhizhen Liu ◽  
Xiaoke Sun
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Crash Severity ◽  
Apriori Algorithm ◽  
Driving Mode ◽  
Extreme Gradient Boosting ◽  
The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

Detection and defense of cyberattacks on the machine learning control of robotic systems

2021 ◽  
pp. 154851292110438
Author(s):  
George W Clark ◽  
Todd R Andel ◽  
J Todd McDonald ◽  
Tom Johnsten ◽  
Tom Thomas
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Defense Mechanisms ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Robotic Systems ◽  
Machine Learning Model ◽  
Attack Surface

Robotic systems are no longer simply built and designed to perform sequential repetitive tasks primarily in a static manufacturing environment. Systems such as autonomous vehicles make use of intricate machine learning algorithms to adapt their behavior to dynamic conditions in their operating environment. These machine learning algorithms provide an additional attack surface for an adversary to exploit in order to perform a cyberattack. Since an attack on robotic systems such as autonomous vehicles have the potential to cause great damage and harm to humans, it is essential that detection and defenses of these attacks be explored. This paper discusses the plausibility of direct and indirect cyberattacks on a machine learning model through the use of a virtual autonomous vehicle operating in a simulation environment using a machine learning model for control. Using this vehicle, this paper proposes various methods of detection of cyberattacks on its machine learning model and discusses possible defense mechanisms to prevent such attacks.

scholarly journals Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 748 ◽  
Author(s):  
John E. Ball ◽  
Bo Tang
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Embedded Computing ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Advanced driver assistance systems (ADAS) are rapidly being developed for autonomous vehicles [...]

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