scholarly journals Effects of Marking Automated Vehicles on Human Drivers on Highways

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 286 ◽  
Author(s):  
Tanja Fuest ◽  
Alexander Feierle ◽  
Elisabeth Schmidt ◽  
Klaus Bengler
Keyword(s):  
Road Traffic ◽  
Driving Behavior ◽  
Sensor Technology ◽  
Lane Change ◽  
Objective Data ◽  
Automated Vehicles ◽  
Traffic Jam ◽  
Automated Vehicle ◽  
Time Headway ◽  
First Time

Due to the short range of the sensor technology used in automated vehicles, we assume that the implemented driving strategies may initially differ from those of human drivers. Nevertheless, automated vehicles must be able to move safely through manual road traffic. Initially, they will behave as carefully as human learners do. In the same way that driving-school vehicles tend to be marked in Germany, markings for automated vehicles could also prove advantageous. To this end, a simulation study with 40 participants was conducted. All participants experienced three different highway scenarios, each with and without a marked automated vehicle. One scenario was based around some roadworks, the next scenario was a traffic jam, and the last scenario involved a lane change. Common to all scenarios was that the automated vehicles strictly adhered to German highway regulations, and therefore moved in road traffic somewhat differently to human drivers. After each trial, we asked participants to rate how appropriate and disturbing the automated vehicle’s driving behavior was. We also measured objective data, such as the time of a lane change and the time headway. The results show no differences for the subjective and objective data regarding the marking of an automated vehicle. Reasons for this might be that the driving behavior itself is sufficiently informative for humans to recognize an automated vehicle. In addition, participants experienced the automated vehicle’s driving behavior for the first time, and it is reasonable to assume that an adjustment of the humans’ driving behavior would take place in the event of repeated encounters.

Cellular Automata–Based Modeling and Simulation of a Mixed Traffic Flow of Manual and Automated Vehicles

2017 ◽  
Vol 2622 (1) ◽  
pp. 105-116 ◽  
Author(s):  
Da Yang ◽  
Xiaoping Qiu ◽  
Lina Ma ◽  
Danhong Wu ◽  
Liling Zhu ◽  
...  
Keyword(s):  
Reaction Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Flow Characteristics ◽  
Lane Change ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Obvious Effect ◽  
Mixed Traffic Flow ◽  
Automated Vehicle

In recent years, automated vehicles have been developing rapidly, and some automated vehicles have begun to drive on highways. The market share of automated vehicles is expected to increase and will greatly affect traffic flow characteristics. This paper focuses on the mixed traffic flow of manual and automated vehicles. The study improves the existing cellular automaton model to capture the differences between manual vehicles and automated vehicles. Computer simulations are employed to analyze the characteristic variations in the mixed traffic flow under different automated vehicle proportions, lane change probabilities, and reaction times. Several new conclusions are drawn in the paper. First, with the increment of the proportion of automated vehicles, freeway capacity increases; the capacity increment is more significant for single-lane traffic than for two-lane traffic. Second, for single-lane traffic flow, reducing the reaction time of the automated vehicle can significantly improve road traffic capacity—as much as doubling it—and reaction time reduction has no obvious effect on the capacity of the two-lane traffic. Third, with the proportion increment of automated vehicles, lane change frequency reduces significantly. Fourth, when the density is 15 < ρ < 55 vehicles/km, the addition of 20% automated vehicles to a traffic flow that consisted of only manual vehicles can decrease congestion by up to 16.7%.

scholarly journals From Driverless Dilemmas to More Practical Common-Sense Tests for Automated Vehicles

2020 ◽  
Author(s):  
Julian De Freitas ◽  
Bryant Walker Smith ◽  
Andrea Censi ◽  
Luigi Di Lillo ◽  
Sam E. Anthony ◽  
...  
Keyword(s):  
Common Sense ◽  
Autonomous Systems ◽  
Driving Behavior ◽  
Automated Vehicles ◽  
Everyday Lives ◽  
The Common ◽  
High Level ◽  
First Time ◽  
Trolley Dilemmas ◽  
Relevant Component

For the first time in history, automated vehicles (AVs) are being deployed in populated environments. This unprecedented transformation of our everyday lives demands a significant undertaking: endowing complex autonomous systems with ethically acceptable behavior. We outline how one prominent, ethically-relevant component of AVs—driving behavior—is inextricably linked to stakeholders in the technical, regulatory, and social spheres of the field. Whereas humans are presumed (rightly or wrongly) to have the ‘common sense’ to behave ethically in new driving situations beyond a standard driving test, AVs do not (and probably should not) enjoy this presumption. We examine, at a high level, how to test the common sense of an AV. We start by reviewing discussions of ‘driverless dilemmas’, adaptions of the traditional ‘trolley dilemmas’ of philosophy that have sparked discussion on AV ethics but have limited use to the technical and legal spheres. Then, we explain how to substantially change the premises and features of these dilemmas (while preserving their behavioral diagnostic spirit) in order to lay the foundations for a more practical and relevant framework that tests driving common sense as an integral part of road rules testing.

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

scholarly journals Autonomous and Automated Vehicles in Germany and Hungary, with Special Attention to the Question of Civil Liability

2021 ◽  
Vol 10 (1) ◽  
pp. 55-64
Author(s):  
Béla Csitei
Keyword(s):  
Autonomous Vehicles ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Civil Liability ◽  
Automated Vehicles ◽  
Automated Vehicle ◽  
Legal Literature ◽  
The World ◽  
History Of ◽  
Definition Of

The most frequent questions associated with autonomous vehicles both in the world press and in legal literature are those that look for the answer as to who is responsible for the accidents caused by these machines. However, only a few such questions deal with the issue that all factums apply different definitions, and the terminology is the basis of applying the particular factum. So, among others, answering the question is inevitable as to whether the autonomous or automated vehicle can be considered a ‘vehicle’, or the human sitting in the car can be considered the ‘driver’. If we decide not to consider the autonomous vehicle to be a vehicle, and – ad absurdum – we create an independent, sui generis category of vehicles, then the legal factums regarding the definition of the vehicle will not be applicable to the factum concerning the history of autonomous vehicles; however, their applicability will surely be questioned. With regard to this, I focus in my study on how the German Road Traffic Act (Straßenverkehrsgesetz) accommodates more advanced automated vehicles, and after this I compare the Hungarian and German rules that are relevant in terms of civil liability if we study the vehicles in question.

scholarly journals Comparison of Methods to Evaluate the Influence of an Automated Vehicle’s Driving Behavior on Pedestrians: Wizard of Oz, Virtual Reality, and Video

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 291 ◽  
Author(s):  
Tanja Fuest ◽  
Elisabeth Schmidt ◽  
Klaus Bengler
Keyword(s):  
Virtual Reality ◽  
Driving Behavior ◽  
Automated Vehicles ◽  
Recognition Time ◽  
Wizard Of Oz ◽  
Traffic System ◽  
Automated Vehicle ◽  
The Road ◽  
Road Users ◽  
Shared Space

Integrating automated vehicles into mixed traffic entails several challenges. Their driving behavior must be designed such that is understandable for all human road users, and that it ensures an efficient and safe traffic system. Previous studies investigated these issues, especially regarding the communication between automated vehicles and pedestrians. These studies used different methods, e.g., videos, virtual reality, or Wizard of Oz vehicles. However, the extent of transferability between these studies is still unknown. Therefore, we replicated the same study design in four different settings: two video, one virtual reality, and one Wizard of Oz setup. In the first video setup, videos from the virtual reality setup were used, while in the second setup, we filmed the Wizard of Oz vehicle. In all studies, participants stood at the roadside in a shared space. An automated vehicle approached from the left, using different driving profiles characterized by changing speed to communicate its intention to let the pedestrians cross the road. Participants were asked to recognize the intention of the automated vehicle and to press a button as soon as they realized this intention. Results revealed differences in the intention recognition time between the four study setups, as well as in the correct intention rate. The results from vehicle–pedestrian interaction studies published in recent years that used different study settings can therefore only be compared to each other to a limited extent.

scholarly journals From driverless dilemmas to more practical commonsense tests for automated vehicles

2021 ◽  
Vol 118 (11) ◽  
pp. e2010202118
Author(s):  
Julian De Freitas ◽  
Andrea Censi ◽  
Bryant Walker Smith ◽  
Luigi Di Lillo ◽  
Sam E. Anthony ◽  
...  
Keyword(s):  
Common Sense ◽  
Autonomous Systems ◽  
Driving Behavior ◽  
Automated Vehicles ◽  
Everyday Lives ◽  
The Common ◽  
High Level ◽  
First Time ◽  
Trolley Dilemmas ◽  
Relevant Component

For the first time in history, automated vehicles (AVs) are being deployed in populated environments. This unprecedented transformation of our everyday lives demands a significant undertaking: endowing complex autonomous systems with ethically acceptable behavior. We outline how one prominent, ethically relevant component of AVs—driving behavior—is inextricably linked to stakeholders in the technical, regulatory, and social spheres of the field. Whereas humans are presumed (rightly or wrongly) to have the “common sense” to behave ethically in new driving situations beyond a standard driving test, AVs do not (and probably should not) enjoy this presumption. We examine, at a high level, how to test the common sense of an AV. We start by reviewing discussions of “driverless dilemmas,” adaptions of the traditional “trolley dilemmas” of philosophy that have sparked discussion on AV ethics but have limited use to the technical and legal spheres. Then, we explain how to substantially change the premises and features of these dilemmas (while preserving their behavioral diagnostic spirit) in order to lay the foundations for a more practical and relevant framework that tests driving common sense as an integral part of road rules testing.

scholarly journals Effects of gender, age, experience, and practice on driver reaction and acceptance of traffic jam chauffeur systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Husam Muslim ◽  
Makoto Itoh ◽  
Cho Kiu Liang ◽  
Jacobo Antona-Makoshi ◽  
Nobuyuki Uchida
Keyword(s):  
Traffic Congestion ◽  
Simulation Experiment ◽  
Demographic Factors ◽  
Driving Simulation ◽  
Lane Change ◽  
Automated Driving ◽  
Left Hand ◽  
Traffic Jam ◽  
Automated Vehicle ◽  
The Individual

AbstractThis study conducted a driving simulation experiment to compare four automated driving systems (ADS) designs during lane change demanding traffic situations on highways while accounting for the drivers’ gender, age, experience, and practice. A lane-change maneuver was required when the automated vehicle approaches traffic congestion on the left-hand lane. ADS-1 can only reduce the speed to synchronize with the congestion. ADS-2 reduces the speed and issues an optional request to intervene, advising the driver to change lanes manually. ADS-3 offers to overtake the congestion autonomously if the driver approves it. ADS-4 overtakes the congestion autonomously without the driver’s approval. Results of drivers’ reaction, acceptance, and trust indicated that differences between ADS designs increase when considering the combined effect of drivers’ demographic factors more than the individual effect of each factor. However, the more ADS seems to have driver-like capacities, the more impact of demographic factors is expected. While preliminary, these findings may help us understand how ADS users’ behavior can differ based on the interaction between human demographic factors and system design.

scholarly journals Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Johannes Ossig ◽  
Stephanie Cramer ◽  
Klaus Bengler
Keyword(s):  
Driving Behavior ◽  
Automation System ◽  
Automated Driving ◽  
Driving Style ◽  
Driving Experience ◽  
Automated Vehicle ◽  
Future Design ◽  
Vehicle System ◽  
The Future ◽  
Conceptual Distinction

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

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