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.

scholarly journals Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2405
Author(s):  
Heung-Gu Lee ◽  
Dong-Hyun Kang ◽  
Deok-Hwan Kim
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Response Speed ◽  
Autonomous Driving ◽  
Human Machine Interface ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
Driving Assistance ◽  
Assistant Systems ◽  
Machine Interface

Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

Unsettled Issues on HD Mapping Technology for Autonomous Driving and ADAS

2021 ◽  
Keyword(s):  
Autonomous Driving ◽  
Full Potential ◽  
Driver Assistance ◽  
Automated Driving ◽  
High Definition ◽  
Driver Assistance Systems ◽  
Resource Needs ◽  
Mapping Technology ◽  
Assistance Systems ◽  
Multiple Challenges

Current advanced driver-assistance systems (ADAS) and automated driving systems (ADS) rely on high-definition (HD) maps to enable a range of features and functions. These maps can be viewed as an additional sensor from an ADAS or ADS perspective as they impact overall system confidence, reduce system computational resource needs, help improve comfort and convenience, and ultimately contribute to system safety. However, HD mapping technology presents multiple challenges to the automotive industry. Unsettled Issues on HD Mapping Technology for Autonomous Driving and ADAS identifies the current unsettled issues that need to be addressed to reach the full potential of HD maps for ADAS and ADS technology and suggests some possible solutions for initial map creation, map change detection and updates, and map safety levels.

scholarly journals High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1131
Author(s):  
Eduardo Sánchez Morales ◽  
Julian Dauth ◽  
Bertold Huber ◽  
Andrés García Higuera ◽  
Michael Botsch
Keyword(s):  
State Estimation ◽  
Autonomous Vehicles ◽  
Current Trend ◽  
Research Work ◽  
Autonomous Driving ◽  
Indoor Navigation ◽  
Global Navigation Satellite Systems ◽  
Navigation Systems ◽  
Automated Driving ◽  
Satellite Systems

A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK.

scholarly journals Takeover Safety Analysis with Driver Monitoring Systems and Driver-Vehicle Interfaces in Highly Automated Vehicles

2021 ◽  
Vol 11 (15) ◽  
pp. 6685
Author(s):  
Dongyeon Yu ◽  
Chanho Park ◽  
Hoseung Choi ◽  
Donggyu Kim ◽  
Sung-Ho Hwang
Keyword(s):  
Monitoring System ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Clear Correlation ◽  
Total System ◽  
System Failure ◽  
Automated Driving ◽  
Minimum Risk ◽  
Human In The Loop ◽  
Driver Monitoring

According to SAE J3016, autonomous driving can be divided into six levels, and partially automated driving is possible from level three up. A partially or highly automated vehicle can encounter situations involving total system failure. Here, we studied a strategy for safe takeover in such situations. A human-in-the-loop simulator, driver-vehicle interface, and driver monitoring system were developed, and takeover experiments were performed using various driving scenarios and realistic autonomous driving situations. The experiments allowed us to draw the following conclusions. The visual–auditory–haptic complex alarm effectively delivered warnings and had a clear correlation with the user’s subjective preferences. There were scenario types in which the system had to immediately enter minimum risk maneuvers or emergency maneuvers without requesting takeover. Lastly, the risk of accidents can be reduced by the driver monitoring system that prevents the driver from being completely immersed in non-driving-related tasks. We proposed a safe takeover strategy from these results, which provides meaningful guidance for the development of autonomous vehicles. Considering the subjective questionnaire evaluations of users, it is expected to improve the acceptance of autonomous vehicles and increase the adoption of autonomous vehicles.

scholarly journals Model-based estimation of the state of vehicle automation as derived from the driver’s spontaneous visual strategies

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Damien Schnebelen ◽  
Camilo Charron ◽  
Franck Mars
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
The State ◽  
Partial Least Square ◽  
Least Square ◽  
Transition Matrices ◽  
Automated Driving ◽  
Gaze Behaviour ◽  
Motor Actions ◽  
Dynamic Indicators

When manually steering a car, the driver’s visual perception of the driving scene and his or her motor actions to control the vehicle are closely linked. Since motor behaviour is no longer required in an automated vehicle, the sampling of the visual scene is affected. Autonomous driving typically results in less gaze being directed towards the road centre and a broader exploration of the driving scene, compared to manual driving. To examine the corollary of this situation, this study estimated the state of automation (manual or automated) on the basis of gaze behaviour. To do so, models based on partial least square regressions were computed by considering the gaze behaviour in multiple ways, using static indicators (percentage of time spent gazing at 13 areas of interests), dynamic indicators (transition matrices between areas) or both together. Analysis of the quality of predictions for the different models showed that the best result was obtained by considering both static and dynamic indicators. However, gaze dynamics played the most important role in distinguishing between manual and automated driving. This study may be relevant to the issue of driver monitoring in autonomous vehicles.

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.

The Influence of Autonomous Vehicles on Insurers and Ride-Hailing Services

2020 ◽  
Vol 22 ◽  
Author(s):  
Carlos Eduardo Alfonzo
Keyword(s):  
Autonomous Vehicles ◽  
Risk Sharing ◽  
Business Models ◽  
Service Providers ◽  
Insurance Industry ◽  
Autonomous Driving ◽  
Insurance Companies ◽  
Current Status ◽  
Complete Automation ◽  
Car Insurance

Autonomous vehicles have captured the public’s imagination for what they could do to change the way people move from one place to another. Relative newcomers to the auto industry, such as Tesla, Alphabet, and Uber, have been developing software seeking to power fully autonomous vehicles. The implication is that there would be no need for a driver. Driverless vehicles pose a number of issues and opportunities for transportation companies and their affiliate industries such as car insurance companies. Traditional automakers such as Ford and BMW are partnering with both other automakers and technology companies in order to engage synergies and achieve complete automation before their competitors. With this competition underway, there are sectors in the economy whose current business models would be disrupted by autonomous vehicles. One of these is the car insurance industry, which depends on sharing risk with policy holders. This paper delves into the topic of how insurance companies would be affected by a change in this risk-sharing relationship with policy holders. Furthermore, this paper also examines the current status of the ride-hailing industry and how the main service providers are aiming to benefit from autonomous driving technology.

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.

scholarly journals Sitting Postures and Activities in Autonomous Vehicles – New Requirements towards Occupant Safety

2019 ◽  
Vol 63 (1) ◽  
pp. 1874-1878
Author(s):  
Anna-Lena Köhler ◽  
Julia Pelzer ◽  
Kristian Seidel ◽  
Stefan Ladwig
Keyword(s):  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
User Study ◽  
Autonomous Driving ◽  
Test Case ◽  
Automated Driving ◽  
Occupant Safety ◽  
Safety Requirements ◽  
Technological Developments ◽  
The Impact

In the context of autonomous driving, new possibilities for passenger positions and occupation arise. Vehicle concepts provide more degrees of freedom for seating configurations and different activities as a passenger, leading to a need for advanced protection principles. The H2020-project OSCCAR analyses occupant safety requirements for highly automated vehicles (HAV) and defines technological developments necessary for novel safety principles. In order to understand the potential of novel sitting postures and activities in the context of autonomous driving, an empirical user study was conducted to examine the impact of different scenarios on preferred sitting postures in a simulated automated driving situation. Results gave insights into detailed sitting postures that are most likely to be obtained by occupants in future use cases. The results serve as input to a test case matrix in order to design future occupant restraint principles.

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