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 Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics

2020 ◽  
Vol 10 (18) ◽  
pp. 6317 ◽  
Author(s):  
Wilfried Wöber ◽  
Georg Novotny ◽  
Lars Mehnen ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Parameter Estimation ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
The Other ◽  
Sensor Data ◽  
Critical Feature ◽  
Motion Data ◽  
Movement Data ◽  
End Use ◽  
Vehicle Movement

On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle’s movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions.

Sensor Fusion Algorithm that Has Resilience under Autonomous Driving Conditions: A Survey

2017 ◽  
Vol 865 ◽  
pp. 429-433
Author(s):  
Sung Bum Park ◽  
Hyeok Chan Kwon ◽  
Dong Hoon Lee
Keyword(s):  
Sensor Fusion ◽  
Autonomous Driving ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Multiple Sensors ◽  
Autonomous Cars ◽  
Driving Conditions

Autonomous cars recognize the surroundings through multiple sensors and make decisions to control the car in order to arrive at destination without driver's interventions. In such environment, if sensor data forgery occurs, it could lead to a big (critical) accident that could threaten the life of the driver. In the paper, a research on a way to get accurate driving information through sensor fusion algorithm that has resilience against data forgery and modulation will be discussed.

MPC-Based Control of Autonomous Vehicles With Localized Path Planning for Obstacle Avoidance Under Uncertainties

2019 ◽  
Author(s):  
Sai Rajeev Devaragudi ◽  
Bo Chen
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Sensor Data ◽  
Constant Time ◽  
Longitudinal Motion ◽  
Driver Assistance Systems

Abstract This paper presents a Model Predictive Control (MPC) approach for longitudinal and lateral control of autonomous vehicles with a real-time local path planning algorithm. A heuristic graph search method (A* algorithm) combined with piecewise Bezier curve generation is implemented for obstacle avoidance in autonomous driving applications. Constant time headway control is implemented for a longitudinal motion to track lead vehicles and maintain a constant time gap. MPC is used to control the steering angle and the tractive force of the autonomous vehicle. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller.

The Skeptic and The Madman: The Proto‐Pragmatism of Thomas Reid

2006 ◽  
Vol 4 (2) ◽  
pp. 125-137 ◽  
Author(s):  
Erik Lundestad
Keyword(s):  
Common Sense ◽  
John Dewey ◽  
Charles Sanders Peirce ◽  
The Other ◽  
Thomas Reid ◽  
Other Hand

Even though the philosophy of common sense is not justifi able as such, the assump- tion upon which it rests, namely that there are things which we are not in position to doubt is correct. The reason why Thomas Reid was unable to bring this assumption out in a justifi able manner is that his views, both on knowledge and nature, are to be considered dogmatic. American pragmatists such as Charles Sanders Peirce and John Dewey on the other hand, may be seen as offering us a ‘critical’ and post-Darwinian philosophy of common sense.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

Autonomy and Responsibility in Hybrid Systems

2017 ◽  
Author(s):  
Wulf Loh ◽  
Janina Loh
Keyword(s):  
Hybrid Systems ◽  
Hybrid System ◽  
Moral Dilemma ◽  
Autonomous Driving ◽  
Shared Responsibility ◽  
Driving System ◽  
Autonomous Cars ◽  
Self Driving Cars ◽  
Trolley Cases ◽  
Autonomous Driving System

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

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.

The development of body and organ shape

BMC Zoology ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ansa E. Cobham ◽  
Christen K. Mirth
Keyword(s):  
Relative Position ◽  
Body Shape ◽  
Single Cells ◽  
The Other ◽  
Geometric Features ◽  
Main Text ◽  
Size Characterization ◽  
Organ Shape ◽  
The Many

Abstract Background Organisms show an incredibly diverse array of body and organ shapes that are both unique to their taxon and important for adapting to their environment. Achieving these specific shapes involves coordinating the many processes that transform single cells into complex organs, and regulating their growth so that they can function within a fully-formed body. Main text Conceptually, body and organ shape can be separated in two categories, although in practice these categories need not be mutually exclusive. Body shape results from the extent to which organs, or parts of organs, grow relative to each other. The patterns of relative organ size are characterized using allometry. Organ shape, on the other hand, is defined as the geometric features of an organ’s component parts excluding its size. Characterization of organ shape is frequently described by the relative position of homologous features, known as landmarks, distributed throughout the organ. These descriptions fall into the domain of geometric morphometrics. Conclusion In this review, we discuss the methods of characterizing body and organ shape, the developmental programs thought to underlie each, highlight when and how the mechanisms regulating body and organ shape might overlap, and provide our perspective on future avenues of research.

scholarly journals Modules and Techniques for Motion Planning: An Industrial Perspective

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 420
Author(s):  
Stefano Quer ◽  
Luz Garcia
Keyword(s):  
Data Fusion ◽  
Autonomous Driving ◽  
Random Trees ◽  
Approximation Techniques ◽  
Main Research ◽  
Autonomous Cars ◽  
Critical Issues ◽  
Relative Errors ◽  
Path Planner ◽  
Many Core

Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.

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