scholarly journals Behavior Identification and Prediction for a Probabilistic Risk Framework

2019 ◽  
Author(s):  
Jasprit S. Gill ◽  
Pierluigi Pisu ◽  
Venkat N. Krovi ◽  
Matthias J. Schmid
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Estimation ◽  
Autonomous Vehicle ◽  
Search Space ◽  
Complex Environments ◽  
Current State ◽  
Risk Framework ◽  
Detailed Simulation ◽  
The Right ◽  
State Of The Environment

Abstract Operation in a real world traffic requires the ability to plan motion in complex environments (multiple moving participants) from autonomous vehicles. Navigation through such environments necessitates the provision of the right search space for the trajectory or maneuver planners so that the safest motion for the ego vehicle can be identified. Analyzing risks based on the predicted trajectories of all traffic participants (given the current state of the environment and its participants) aids in the proper formulation of this search space. This study introduces a fresh taxonomy of safety and risk that an autonomous vehicle should be capable of handling. It formulates a reference system architecture for implementation as well as describes a novel way of identifying and predicting the behaviors of other traffic participants utilizing classic Multi Model Adaptive Estimation (MMAE). Detailed simulation results and a discussion about the associated tuning of the implemented model conclude this work.

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 An Overview of Lidar Imaging Systems for Autonomous Vehicles

2019 ◽  
Vol 9 (19) ◽  
pp. 4093 ◽  
Author(s):  
Santiago Royo ◽  
Maria Ballesta-Garcia
Keyword(s):  
Autonomous Vehicles ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Single Point ◽  
Imaging Systems ◽  
Light Sources ◽  
Final Solution ◽  
Large Uncertainty ◽  
Current State ◽  
The Subject

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.

scholarly journals Soluciones de conducción para vehículos autónomos, y propuestas adaptativas para establecer los tiempos de semáforos y elegir rutas

2019 ◽  
Vol 20 (2) ◽  
Author(s):  
José Gerardo Carrillo González
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Traffic Lights ◽  
Linear Path ◽  
Time Cycle ◽  
Adaptive Solutions ◽  
The Times ◽  
The Right

Two objectives are pursued in this article: 1) with adaptive solutions, improve the traffic flow by setting the time cycle of traffic lights at intersections and reduce the travel time by selecting the vehicles route (treated as separated problems). 2) Avoid driving conflicts among autonomous vehicles (which have defined trajectories) and these with a non-autonomous vehicle (which follows a free path). The traffic lights times are set with formulas that continuously recalculate the times values according the number of vehicles on the intersecting streets. For selecting the vehicles route an algorithm was developed, this calculates different routes (connected streets that conform a solution from the origin to the destination) and selects a route with low density. The results of the article indicate that the adaptive solutions to set the traffic lights times and to select the vehicles path, present a greater traffic flow and a shorter travel time, respectively, than conventional solutions. To avoid collisions among autonomous vehicles which follow a linear path, an algorithm was developed, this was successfully tested in different scenarios through simulations, besides the algorithm allows the interaction of a vehicle manually controlled (circulating without restrictions) with the autonomous vehicles. The algorithm regulates the autonomous vehicles acceleration (deceleration) and assigns the right of way among these and with the human controlled vehicle.

scholarly journals Collective learning from individual experiences and information transfer during group foraging

2019 ◽  
Vol 16 (151) ◽  
pp. 20180803 ◽  
Author(s):  
Andrea Falcón-Cortés ◽  
Denis Boyer ◽  
Gabriel Ramos-Fernández
Keyword(s):  
Information Transfer ◽  
Collective Motion ◽  
Empirical Studies ◽  
Collective Learning ◽  
Complex Environments ◽  
Uncertain Environments ◽  
Modelling Framework ◽  
Current State ◽  
Individual Memory ◽  
State Of The Environment

Living in groups brings benefits to many animals, such as protection against predators and an improved capacity for sensing and making decisions while searching for resources in uncertain environments. A body of studies has shown how collective behaviours within animal groups on the move can be useful for pooling information about the current state of the environment. The effects of interactions on collective motion have been mostly studied in models of agents with no memory. Thus, whether coordinated behaviours can emerge from individuals with memory and different foraging experiences is still poorly understood. By means of an agent-based model, we quantify how individual memory and information fluxes can contribute to improving the foraging success of a group in complex environments. In this context, we define collective learning as a coordinated change of behaviour within a group resulting from individual experiences and information transfer. We show that an initially scattered population of foragers visiting dispersed resources can gradually achieve cohesion and become selectively localized in space around the most salient resource sites. Coordination is lost when memory or information transfer among individuals is suppressed. The present modelling framework provides predictions for empirical studies of collective learning and could also find applications in swarm robotics and motivate new search algorithms based on reinforcement.

Persistent Target Detection and Tracking by an Autonomous Swarm

2018 ◽  
Author(s):  
John J. Gainer ◽  
Levi D. DeVries ◽  
Michael D. Kutzer
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Search Space ◽  
Base Station ◽  
Radioactive Material ◽  
Team Members ◽  
Search Area ◽  
Multiple Vehicles ◽  
Multivehicle Control ◽  
Experimental Trials

This paper presents an autonomous multivehicle control algorithm capable of persistently searching and tracking targets in a defined search area subject to operational endurance constraints of individual agents. The algorithm development is modular to allow scalability and a control architecture that can be modified to any type of autonomous vehicle, search area, or target. In practical application, a target can be anything from heat signatures to radioactive material; therefore, this work employs a generic emitter-detector pair as a placeholder relationship for real world applications. The control strategy accounts for the appearance, motion, and disappearance of multiple targets in the search space constituting the utility of creating a team of multiple search agents. When agent battery level drops below a predetermined threshold, the agent returns to a base station to recharge and be relaunched into the mission. Remaining agents must account for this loss and gain of other team members as they exit the search environment. The contributions of this work are 1) the design of search trajectories for autonomous vehicles with limited endurance, 2) incorporation of return-to-base and recharge time requirements, and 3) coordination of multiple vehicles by developing a decision-making model to and assign agents to operational modes. We have run an extensive number of experimental trials to collect and analyze performance data for further development and testing.

Autonomous vehicles and freight traffic: towards better efficiency of road, rail or urban logistics?

2018 ◽  
Vol 58 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Péter Bucsky
Keyword(s):  
Autonomous Vehicles ◽  
Road Transport ◽  
Transport Sector ◽  
Future Directions ◽  
Car Industry ◽  
Level Of Automation ◽  
Current State ◽  
Freight Traffic ◽  
Levels Of Automation ◽  
Private Car

Abstract The freight transport sector is a low profit and high competition business and therefore has less ability to invest in research and development in the field of autonomous vehicles (AV) than the private car industry. There are already different levels of automation technologies in the transport industry, but most of these are serving niche demands and answers have yet to be found about whether it would be worthwhile to industrialise these technologies. New innovations from different fields are constantly changing the freight traffic industry but these are less disruptive than on other markets. The aim of this article is to show the current state of development of freight traffic with regards to AVs and analyse which future directions of development might be viable. The level of automation is very different in the case of different transport modes and most probably the technology will favour road transport over other, less environmentally harmful traffic modes.

Integrative Approach in Law Understanding: the History of the Origin of Ideas and the Current State

2020 ◽  
Vol 2 (1) ◽  
pp. 115-130
Author(s):  
R. R. Palekha ◽  
Keyword(s):  
Central Place ◽  
Scientific Basis ◽  
Integrative Approach ◽  
Current State ◽  
History Of ◽  
Legal Thinking ◽  
Reliable Knowledge ◽  
The Right ◽  
Fat Formulation

Introduction. Right understanding is the most live, interesting and, at the same time, the uncertain and changeable area of researches which takes the central place as in the general theory of the right, and gains the increasing value in industry jurisprudence that is connected with its considerable teoretiko-methodological and applied potential which is shown in spheres of lawmaking and law-enforcement activity. Thus, right understanding represents research tools of the subject of knowledge which allow to study all range legal and, the based on them, state phenomena for the purpose of obtaining reliable knowledge of state and legal reality. In this regard integrative approach in right understanding which has rich history of the formation and development is of special interest, allows to perceive the right as integrally complete phenomenon, as much as possible retrieves its regulatory abilities and, provides achievement of criteria of scientific research: comprehensiveness, objectivity, historicism. Materials and Methods. In article an attempt of the analysis of integrative approach in right understanding from a position of history of origin of his ideas and assessment of the current state is made. A result of studying of scientific literature, generalization and comparison of the different points of view fat formulation of author’s determination of category “right understanding” and submission of the evidence-based integrative theory of right understanding which as much as possible conforms to requirements of time and has essential regulatory and guarding potential. Results. In article the category right understanding is comprehensively considered, different integrative theories of right understanding from a position of their origin and development are submitted, the value of modern integrative approach in right understanding is shown, perspectives of its further development are evaluated. Discussion and Conclusion. The author comes to the conclusion about the theoretical and methodological consistency and inevitability of the integrative approach in law understanding, which acts as a scientifically grounded type of legal thinking capable of comprehending the law on a truly scientific basis.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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