scholarly journals Inferring the Driver’s Lane Change Intention through LiDAR-Based Environment Analysis Using Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 475
Author(s):  
Alberto Díaz-Álvarez ◽  
Miguel Clavijo ◽  
Felipe Jiménez ◽  
Francisco Serradilla
Keyword(s):  
Autonomous Vehicles ◽  
Lane Change ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Data Set ◽  
Environment Analysis ◽  
Process Support ◽  
Surrounding Environment ◽  
Wide Range ◽  
Input Variables

Most of the tactic manoeuvres during driving require a certain understanding of the surrounding environment from which to devise our future behaviour. In this paper, a Convolutional Neural Network (CNN) approach is used to model the lane change behaviour to identify when a driver is going to perform this manoeuvre. To that end, a slightly modified CNN architecture adapted to both spatial (i.e., surrounding environment) and non-spatial (i.e., rest of variables such as relative speed to the front vehicle) input variables. Anticipating a driver’s lane change intention means it is possible to use this information as a new source of data in wide range of different scenarios. One example of such scenarios might be the decision making process support for human drivers through Advanced Driver Assistance Systems (ADAS) fed with the data of the surrounding cars in an inter-vehicular network. Another example might even be its use in autonomous vehicles by using the data of a specific driver profile to make automated driving more human-like. Several CNN architectures have been tested on a simulation environment to assess their performance. Results show that the selected architecture provides a higher degree of accuracy than random guessing (i.e., assigning a class randomly for each observation in the data set), and it can capture subtle differences in behaviour between different driving profiles.

scholarly journals Method for Gauging Usage Opportunities for Partially Automated Vehicles with Application to Public Travel Survey Data Sets

2017 ◽  
Vol 2625 (1) ◽  
pp. 43-50
Author(s):  
Kenneth P. Laberteaux ◽  
Karim Hamza ◽  
Alan Berger ◽  
Casey L. Brown
Keyword(s):  
Fuzzy Inference ◽  
Automated Driving ◽  
Data Set ◽  
Inference System ◽  
Travel Survey ◽  
Human Driver ◽  
Wide Range ◽  
Light Duty Vehicle ◽  
Vehicle Automation

Vehicle automation has garnered a significant amount of interest in recent years. When the automated driving (AD) capability of a vehicle is assessed, it is important to distinguish between full automation, in which no human driver is required, and partial automation, in which a human driver may be required to intervene or take control of the vehicle occasionally for portions of the trip. This paper presents a method for assessing usage opportunities of partial AD in light-duty vehicle fleets. Key assumptions are that ( a) the longer the time fraction of driving in which AD is active, the better, and ( b) drivers will value having longer contiguous sections of AD-active time over having to frequently regain vehicle control. Given second-by-second records of real-world driving trips, the method uses a fuzzy inference system to estimate the fraction of driving time at a certain quality-of-use level. Performing the quality-of-use assessment for all trips and vehicles in a representative data set can then provide insight into the fraction of the population that would likely find partial AD desirable. To demonstrate the proposed method, data on vehicle trips from public travel surveys in California (California Household Travel Survey) and Atlanta, Georgia (Atlanta Regional Commission Travel Survey), were used along with simplified prototypical models for partial AD. Simulation results were generally in agreement with common perceptions but showed a wide range of possibilities, which could have been narrowed down further when more detailed trip data became available.

Security and Privacy Vulnerabilities in Automated Driving

2020 ◽  
pp. 154-180
Author(s):  
Suchandra Datta
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Security And Privacy ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Rapid Pace ◽  
Assistance Systems ◽  
Software And Hardware ◽  
Security And Reliability ◽  
Almost All

Driver assistance systems are advancing at a rapid pace, and almost all major companies have started investing in developing autonomous vehicles. However, the security and reliability in this field is still uncertain and debatable. A vehicle compromised by the attackers remotely can be easily used to create chaos of epic proportions. An attacker can control brake, accelerate, and even steering, which can lead to catastrophic consequences. Therefore, an autonomous vehicle can be weaponized extremely easily if proper security protocols are not implemented. This chapter gives a very short and brief overview of some of the possible attacks on autonomous vehicle software and hardware and their potential implications.

scholarly journals Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 543 ◽  
Author(s):  
HongIl An ◽  
Jae-il Jung
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
The State ◽  
Lane Change ◽  
Vehicular Communication ◽  
Automated Driving ◽  
Lane Changing ◽  
State Information ◽  
Gradient Learning

Lane changing systems have consistently received attention in the fields of vehicular communication and autonomous vehicles. In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication. A host vehicle, trying to change lanes, receives the state information of the host vehicle and a remote vehicle that are both equipped with vehicular communication devices. A deep deterministic policy gradient learning algorithm in the host vehicle determines the high-level action of the host vehicle from the state information. The proposed system learns straight-line driving and collision avoidance actions without vehicle dynamics knowledge. Finally, we consider the update period for the state information from the host and remote vehicles.

scholarly journals On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

2021 ◽  
Vol 11 (5) ◽  
pp. 2197
Author(s):  
Stefania Santini ◽  
Nicola Albarella ◽  
Vincenzo Maria Arricale ◽  
Renato Brancati ◽  
Aleksandr Sakhnevych
Keyword(s):  
Environmental Conditions ◽  
Autonomous Vehicles ◽  
Heavy Rain ◽  
Autonomous Driving ◽  
The State ◽  
Driver Assistance Systems ◽  
Model Based ◽  
Wide Range ◽  
Road Friction ◽  
Friction Estimation

In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain).

scholarly journals Research in Autonomous Driving – A Historic Bibliometric View of the Research Development in Autonomous Driving

2021 ◽  
Vol 7 (5) ◽  
pp. 27-44
Author(s):  
Sandra Boric ◽  
Edgar Schiebel ◽  
Christian Schlögl ◽  
Michaela Hildebrandt ◽  
Christina Hofer ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Service Providers ◽  
Autonomous Driving ◽  
Research Field ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Research Fronts ◽  
Institute Of Technology ◽  
Number Of Publications

Autonomous driving has become an increasingly relevant issue for policymakers, the industry, service providers, infrastructure companies, and science. This study shows how bibliometrics can be used to identify the major technological aspects of an emerging research field such as autonomous driving. We examine the most influential publications and identify research fronts of scientific activities until 2017 based on a bibliometric literature analysis. Using the science mapping approach, publications in the research field of autonomous driving were retrieved from Web of Science and then structured using the bibliometric software BibTechMon by the AIT (Austrian Institute of Technology). At the time of our analysis, we identified four research fronts in the field of autonomous driving: (I) Autonomous Vehicles and Infrastructure, (II) Driver Assistance Systems, (III) Autonomous Mobile Robots, and (IV) IntraFace, i.e., automated facial image analysis. Researchers were working extensively on technologies that support the navigation and collection of data. Our analysis indicates that research was moving towards autonomous navigation and infrastructure in the urban environment. A noticeable number of publications focused on technologies for environment detection in automated vehicles. Still, research pointed at the technological challenges to make automated driving safe.

scholarly journals Research in Autonomous Driving – A Historic Bibliometric View of the Research Development in Autonomous Driving

2021 ◽  
Vol 7 (5) ◽  
pp. 27-44
Author(s):  
Sandra Boric ◽  
Edgar Schiebel ◽  
Christian Schlögl ◽  
Michaela Hildebrandt ◽  
Christina Hofer ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Service Providers ◽  
Autonomous Driving ◽  
Research Field ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Research Fronts ◽  
Institute Of Technology ◽  
Number Of Publications

Autonomous driving has become an increasingly relevant issue for policymakers, the industry, service providers, infrastructure companies, and science. This study shows how bibliometrics can be used to identify the major technological aspects of an emerging research field such as autonomous driving. We examine the most influential publications and identify research fronts of scientific activities until 2017 based on a bibliometric literature analysis. Using the science mapping approach, publications in the research field of autonomous driving were retrieved from Web of Science and then structured using the bibliometric software BibTechMon by the AIT (Austrian Institute of Technology). At the time of our analysis, we identified four research fronts in the field of autonomous driving: (I) Autonomous Vehicles and Infrastructure, (II) Driver Assistance Systems, (III) Autonomous Mobile Robots, and (IV) IntraFace, i.e., automated facial image analysis. Researchers were working extensively on technologies that support the navigation and collection of data. Our analysis indicates that research was moving towards autonomous navigation and infrastructure in the urban environment. A noticeable number of publications focused on technologies for environment detection in automated vehicles. Still, research pointed at the technological challenges to make automated driving safe.

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

2020 ◽  
Author(s):  
Christian Devereux ◽  
Justin Smith ◽  
Kate Davis ◽  
Kipton Barros ◽  
Roman Zubatyuk ◽  
...  
Keyword(s):  
Drug Development ◽  
Autonomous Vehicles ◽  
Potential Energy Surfaces ◽  
Organic Molecules ◽  
Chemical Elements ◽  
Molecular Potential ◽  
Wide Range ◽  
Energy Surfaces ◽  
New Applications ◽  
Physical Phenomena

<p>Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, S) make up ~90% of drug like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~10<sup>6</sup> factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.</p>

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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