scholarly journals Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 449 ◽  
Author(s):  
Joaquín López ◽  
Pablo Sánchez-Vilariño ◽  
Rafael Sanz ◽  
Enrique Paz
Keyword(s):  
Internal State ◽  
Autonomous Driving ◽  
Programming Environment ◽  
Graphical Interface ◽  
Disabled People ◽  
Driving Behaviors ◽  
Execution Tracing ◽  
Net Framework ◽  
Inter Process Communication ◽  
Traffic Rules

Most autonomous car control frameworks are based on a middleware layer with several independent modules that are connected by an inter-process communication mechanism. These modules implement basic actions and report events about their state by subscribing and publishing messages. Here, we propose an executive module that coordinates the activity of these modules. This executive module uses hierarchical interpreted binary Petri nets (PNs) to define the behavior expected from the car in different scenarios according to the traffic rules. The module commands actions by sending messages to other modules and evolves its internal state according to the events (messages) received. A programming environment named RoboGraph (RG) is introduced with this architecture. RG includes a graphical interface that allows the edition, execution, tracing, and maintenance of the PNs. For the execution, a dispatcher loads these PNs and executes the different behaviors. The RG monitor that shows the state of all the running nets has proven to be very useful for debugging and tracing purposes. The whole system has been applied to an autonomous car designed for elderly or disabled people.

scholarly journals Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 437
Author(s):  
Yuya Onozuka ◽  
Ryosuke Matsumi ◽  
Motoki Shino
Keyword(s):  
Visual Information ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Weighting Method ◽  
Personal Mobility ◽  
Human Understanding ◽  
Autonomous Mobility ◽  
Weakly Supervised ◽  
Traffic Rules

Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.

scholarly journals Shifting from closed-source graphical-interface to open-source programming environment: a brief tutorial on running Maxent in R

2017 ◽  
Author(s):  
Xiao Feng ◽  
Fikirte Gebresenbet ◽  
Cassondra Walker
Keyword(s):  
Open Source ◽  
Ecological Niche Modeling ◽  
Programming Environment ◽  
Graphical Interface ◽  
Limited Information ◽  
Computing Environment ◽  
New Methods ◽  
Software Algorithms ◽  
The Relationship ◽  
Closed Source

Ecological niche modeling (ENM) is increasingly being used in studying the relationship between species distributions and environmental conditions. The development of ENM software/algorithms is heading toward open-source programming, for the advantage of efficiency in handling big data and incorporating new methods. Maxent is one of the commonly used ENM algorithms, but there has been limited information and efforts in implementing Maxent in an open-source programming environment (e.g., R). Therefore, we aim to fill the gap of knowledge for using Maxent in R. More specifically, we demonstrate the general implementation of Maxent in R based on a commonly used ENM procedure, provide a function that bridges the Maxent algorithm and R computing environment for easier use, and demonstrate the manipulation of a few crucial Maxent parameters in R. We expect our efforts will promote a shift of the Maxent user community from a graphical-interface to open-source programming.

scholarly journals Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254047
Author(s):  
Yi Guo ◽  
Xiaolan Wang ◽  
Yongmao Huang ◽  
Liang Xu
Keyword(s):  
Ensemble Learning ◽  
Traffic Safety ◽  
Clustering Algorithms ◽  
Autonomous Driving ◽  
Classification Performance ◽  
Majority Voting ◽  
Classification Method ◽  
Driving Style ◽  
Driving Behaviors ◽  
Stable Performance

The classification of driving styles plays a fundamental role in evaluating drivers’ driving behaviors, which is of great significance to traffic safety. However, it still suffers from various challenges, including the insufficient accuracy of the model, the large amount of training parameters, the instability of classification results, and some others. To evaluate the driving behaviors accurately and efficiently, and to study the differences of driving behaviors among various vehicle drivers, a collaborative driving style classification method, which is enabled by ensemble learning and divided into pre-classification and classification, is proposed in this paper. In the pre-classification process, various clustering algorithms are utilized compositely to label some typical initial data with specific labels as aggressive, stable and conservative. Then, in the classification process, other unlabeled data can be classified accurately and efficiently by the majority voting ensemble learning method incorporating three different conventional classifiers. The availability and efficiency of the proposed method are demonstrated through some simulation experiments, in which the proposed collaborative classification method achieves quite good and stable performance on driving style classification. Particularly, compared with some other similar classification methods, the evaluation indicators of the proposed method, including accuracy, precision, recall and F-measure, are improved by 1.49%, 2.90%, 5.32% and 4.49% respectively, making it the best overall performance. Therefore, the proposed method is much preferred for the autonomous driving and usage-based insurance.

scholarly journals Comparing driving behavior of humans and autonomous driving in a professional racing simulator

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245320
Author(s):  
Adrian Remonda ◽  
Eduardo Veas ◽  
Granit Luzhnica
Keyword(s):  
Vehicle Dynamics ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Driving Performance ◽  
Aggressive Driving ◽  
Telemetry Data ◽  
Driving Behaviors ◽  
Extensive Analysis ◽  
Driving Scenario ◽  
Training Professional

Motorsports have become an excellent playground for testing the limits of technology, machines, and human drivers. This paper presents a study that used a professional racing simulator to compare the behavior of human and autonomous drivers under an aggressive driving scenario. A professional simulator offers a close-to-real emulation of underlying physics and vehicle dynamics, as well as a wealth of clean telemetry data. In the first study, the participants’ task was to achieve the fastest lap while keeping the car on the track. We grouped the resulting laps according to the performance (lap-time), defining driving behaviors at various performance levels. An extensive analysis of vehicle control features obtained from telemetry data was performed with the goal of predicting the driving performance and informing an autonomous system. In the second part of the study, a state-of-the-art reinforcement learning (RL) algorithm was trained to control the brake, throttle and steering of the simulated racing car. We investigated how the features used to predict driving performance in humans can be used in autonomous driving. Our study investigates human driving patterns with the goal of finding traces that could improve the performance of RL approaches. Conversely, they can also be applied to training (professional) drivers to improve their racing line.

Adapting to the Traffic Swarm

2020 ◽  
pp. 1391-1414
Author(s):  
Fritz Ulbrich ◽  
Simon Sebastian Rotter ◽  
Raul Rojas
Keyword(s):  
Path Planning ◽  
Common Sense ◽  
Relative Position ◽  
Autonomous Driving ◽  
The Other ◽  
Sensor Data ◽  
Processing Pipeline ◽  
Swarm Behavior ◽  
Autonomous Cars ◽  
Traffic Rules

Swarm behavior can be applied to many aspects of autonomous driving: e.g. localization, perception, path planning or mapping. A reason for this is that from the information observed by swarm members, e.g. the relative position and speed of other cars, further information can be derived. In this chapter the processing pipeline of a “swarm behavior module” is described step by step from selecting and abstracting sensor data to generating a plan – a drivable trajectory – for an autonomous car. Such a swarm-based path planning can play an important role in a scenario where there is a mixture of human drivers and autonomous cars. Experienced human drivers flow with the traffic and adapt their driving to the environment. They do not follow the traffic rules as strictly as computers do, but they are often using common sense. Autonomous cars should not provoke dangerous situations by sticking absolutely to the traffic rules, they must adapt their behavior with respect to the other drivers around them and thus merge with the traffic swarm.

scholarly journals Shifting from closed-source graphical-interface to open-source programming environment: a brief tutorial on running Maxent in R

2017 ◽  
Author(s):  
Xiao Feng ◽  
Fikirte Gebresenbet ◽  
Cassondra Walker
Keyword(s):  
Open Source ◽  
Ecological Niche Modeling ◽  
Programming Environment ◽  
Graphical Interface ◽  
Limited Information ◽  
Computing Environment ◽  
New Methods ◽  
Software Algorithms ◽  
The Relationship ◽  
Closed Source

Ecological niche modeling (ENM) is increasingly being used in studying the relationship between species distributions and environmental conditions. The development of ENM software/algorithms is heading toward open-source programming, for the advantage of efficiency in handling big data and incorporating new methods. Maxent is one of the commonly used ENM algorithms, but there has been limited information and efforts in implementing Maxent in an open-source programming environment (e.g., R). Therefore, we aim to fill the gap of knowledge for using Maxent in R. More specifically, we demonstrate the general implementation of Maxent in R based on a commonly used ENM procedure, provide a function that bridges the Maxent algorithm and R computing environment for easier use, and demonstrate the manipulation of a few crucial Maxent parameters in R. We expect our efforts will promote a shift of the Maxent user community from a graphical-interface to open-source programming.

scholarly journals Steganographic Methods Resistant to JPEG Compression

2021 ◽  
pp. 102-105
Author(s):  
M.V. Kurkina ◽  
I.V. Ponomarev ◽  
D.I. Strokin
Keyword(s):  
Information Technologies ◽  
Programming Environment ◽  
Data Confidentiality ◽  
Mathematical Methods ◽  
C Programming Language ◽  
Main Requirement ◽  
C Programming ◽  
Entire Sequence ◽  
Image Pixels ◽  
Net Framework

The active development of computer systems and information technologies implies an increase in the importance of ensuring the protection and security of information during its transfer or storage. Today, the fulfillment of the necessary data confidentiality requirements tends to the direction of cryptography and steganography. The choice of the applied method of encoding information directly depends on the goals of the user, as well as the available software. The main requirement for the message encoding process is the availability of acceptable computational complexity of the implementation. The article discusses methods of ensuring data confidentiality employing digital steganography, using BMP images as container files, and carrying out their subsequent compression to the JPEG format without losing hidden information. Mathematical methods of constructing a stegosystem for encoding ASCII are studied — a message, each character of which is encoded with exactly one byte. As a result, a computer program was created based on the Microsoft Visual Studio 2017 programming environment and the C# programming language (.NET Framework). The stereotype, in this case, is a sequence of steps between image pixels, into which information is embedded. In this case, the algorithm allows you to memorize not the entire sequence of steps (that is, not the entire keystroke), but only the first five characters of the key or half of the key.

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