scholarly journals Exploiting Machine Learning for Vision and Motion Planning of Autonomous Vehicles Navigation

2021 ◽  
Vol 19 (3) ◽  
pp. 95-104
Author(s):  
M. Rutendo ◽  
◽  
M. A. Al Akkad ◽  
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Human Error ◽  
Autonomous Vehicle ◽  
Good Tool ◽  
The Road ◽  
Web Camera ◽  
Gaming Environment ◽  
Error Perception ◽  
Autonomous Vehicle Navigation

The object of this paper is to create a system that can control any vehicle in any gaming environment to simulate, study, experiment and improve how self-driving vehicles operate. It is to be taken as the bases for future work on autonomous vehicles with real hardware devices. The long-term goal is to eliminate human error. Perception, localisation, planning and control subsystems were developed. LiDAR and RADAR sensors were used in addition to a normal web Camera. After getting information from the perception module, the system will be able to localise where the vehicle is, then the planning module is used to plan to which location the vehicle will move, using localisation module data to draw up the best path to use. After knowing the best path, the system will control the vehicle to move autonomously without human help. As a controller a Proportional Integral Derivative PID controller was used. Python programming language, computer vision, and machine learning were used in developing the system, where the only hardware required is a computer with a GPU and powerful graphical card that can run a game which has a vehicle, roads with lane lines and a map of the road. The developed system is intended to be a good tool in conducting experiments for achieving reliable autonomous vehicle navigation.

scholarly journals Model Contrast of Autonomous Vehicle Impacts on Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Derek Hungness ◽  
Raj Bridgelall
Keyword(s):  
Autonomous Vehicles ◽  
Travel Demand ◽  
Autonomous Vehicle ◽  
Sociodemographic Factors ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Traffic Conditions ◽  
Demand Models ◽  
Wide Range ◽  
Potential Benefits

The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.

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.

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.

Grid-based Environment Estimation for Local Autonomous Vehicle Navigation

2016 ◽  
Author(s):  
Georg Tanzmeister
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Local Environment ◽  
Sensor Data ◽  
Test Vehicle ◽  
Safety Critical ◽  
Environment Model ◽  
Reliable Model ◽  
Autonomous Vehicle Navigation ◽  
Grid Based

This dissertation is focused on the environment model for automated vehicles. A reliable model of the local environment available in real-time is a prerequisite to enable almost any useful ­activity performed by a robot, such as planning motions to fulfill tasks. It is particularly important in safety critical applications, such as for autonomous vehicles in regular traffic. In this thesis, novel concepts for local mapping, tracking, the detection of principal moving directions, cost evaluations in motion planning, and road course estimation have been developed. An object- and sensor-independent grid representation forms the basis of all presented methods enabling a generic and robust estimation of the environment. All approaches have been evaluated with sensor data from real road scenarios, and their performance has been experimentally demonstrated with a test vehicle. ...

scholarly journals Road Segmentation using Semantic Segmentation Networks for ADAS

2019 ◽  
Vol 8 (11) ◽  
pp. 1740-1743
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Warning System ◽  
Semantic Segmentation ◽  
Boundary Line ◽  
Segmentation Result ◽  
Safe Driving ◽  
The Road ◽  
Straight Line ◽  
Road Segmentation

In this paper, we propose a method to automatically segment the road area from the input road images to support safe driving of autonomous vehicles. In the proposed method, the semantic segmentation network (SSN) is trained by using the deep learning method and the road area is segmented by utilizing the SSN. The SSN uses the weights initialized from the VGC-16 network to create the SegNet network. In order to fast the learning time and to obtain results, the class is simplified and learned so that it can be divided into two classes as the road area and the non-road area in the trained SegNet CNN network. In order to improve the accuracy of the road segmentation result, the boundary line of the road region with the straight-line component is detected through the Hough transform and the result is shown by dividing the accurate road region by combining with the segmentation result of the SSN. The proposed method can be applied to safe driving support by autonomously driving the autonomous vehicle by automatically classifying the road area during operation and applying it to the road area departure warning system

scholarly journals Sensors and Sensor’s Fusion in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6586
Author(s):  
Andrzej Stateczny ◽  
Marta Wlodarczyk-Sielicka ◽  
Pawel Burdziakowski
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Navigation ◽  
Autonomous Vehicle Navigation

Autonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications [...]

scholarly journals Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions

2020 ◽  
Author(s):  
Fanta Camara ◽  
Charles Fox
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Utility Functions ◽  
Hyperbolic Function ◽  
The Road ◽  
Definition Of ◽  
Public Datasets ◽  
Discrete Theory ◽  
And Control ◽  
Road Crossing

AbstractUnderstanding pedestrian proxemic utility and trust will help autonomous vehicles to plan and control interactions with pedestrians more safely and efficiently. When pedestrians cross the road in front of human-driven vehicles, the two agents use knowledge of each other’s preferences to negotiate and to determine who will yield to the other. Autonomous vehicles will require similar understandings, but previous work has shown a need for them to be provided in the form of continuous proxemic utility functions, which are not available from previous proxemics studies based on Hall’s discrete zones. To fill this gap, a new Bayesian method to infer continuous pedestrian proxemic utility functions is proposed, and related to a new definition of ‘physical trust requirement’ (PTR) for road-crossing scenarios. The method is validated on simulation data then its parameters are inferred empirically from two public datasets. Results show that pedestrian proxemic utility is best described by a hyperbolic function, and that trust by the pedestrian is required in a discrete ‘trust zone’ which emerges naturally from simple physics. The PTR concept is then shown to be capable of generating and explaining the empirically observed zone sizes of Hall’s discrete theory of proxemics.

Pedestrians’ understanding of fully autonomous vehicles (FAV) intent to stop

2020 ◽  
Vol 64 (1) ◽  
pp. 1931-1932
Author(s):  
Michal Hochman ◽  
Tal Oron-Gilad
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Background Color ◽  
Urban Road ◽  
The Road ◽  
Message Type ◽  
On The Road ◽  
Future Work ◽  
Road Crossing ◽  
Distance Conditions

This study explored pedestrians’ understanding of Fully Autonomous Vehicle (FAV) intention and what influences their decision to cross. Twenty participants saw fixed simulated urban road crossing scenes with a FAV present on the road. The scenes differed from one another in the FAV’s messages: the external Human-Machine Interfaces (e-HMI) background color, message type and modality, the FAV’s distance from the crossing place, and its size. Eye-tracking data and objective measurements were collected. Results revealed that pedestrians looked at the e-HMI before making their decision; however, they did not always make the decision according to the e-HMIs’ color, instructions (in advice messages), or intention (in status messages). Moreover, when they acted according to the e-HMI proposition, for certain distance conditions, they tended to hesitate before making the decision. Findings suggest that pedestrians’ decision making to cross depends on a combination of the e-HMI implementation and the car distance. Future work should explore the robustness of the findings in dynamic and more complex crossing environments.

scholarly journals An Integrated Model for User State Detection of Subjective Discomfort in Autonomous Vehicles

Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 764-777
Author(s):  
Dario Niermann ◽  
Alexander Trende ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Cornelia Hollander ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Linear Models ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
The Road ◽  
Novel Approach ◽  
Driving Scenario ◽  
On The Road ◽  
Using Data ◽  
Subjective Discomfort

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.

scholarly journals Endangering yourself to save another: A real life ethical dilemma

2018 ◽  
Author(s):  
Igor Radun ◽  
Jenni Radun ◽  
Jyrki Kaistinen ◽  
Jake Olivier ◽  
Göran Kecklund ◽  
...  
Keyword(s):  
Ethical Dilemma ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Real Life ◽  
Survey Study ◽  
Heavy Vehicle ◽  
The Road ◽  
Road Users ◽  
Empathy Score ◽  
Experimental Survey

Unlike hypothetical trolley problem studies and an ongoing ethical dilemma with autonomous vehicles, road users can face similar ethical dilemmas in real life. Swerving a heavy vehicle towards the road-side in order to avoid a head-on crash with a much lighter passenger car is often the only option available which could save lives. However, running off-road increases the probability of a roll-over and endangers the life of the heavy vehicle driver. We have created an experimental survey study in which heavy vehicle drivers randomly received one of two possible scenarios. We found that responders were more likely to report they would ditch their vehicle in order to save the hypothetical driver who fell asleep than to save the driver who deliberately diverted their car towards the participant’s heavy vehicle. Additionally, the higher the empathy score, the higher the probability of ditching a vehicle. Implications for autonomous vehicle programming are discussed.

Sign in / Sign up

Export Citation Format

Share Document