scholarly journals Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation

Safety ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 3
Author(s):  
Niklas Grabbe ◽  
Alain Gales ◽  
Michael Höcher ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Driving Simulator ◽  
Road Traffic ◽  
Structured Interviews ◽  
Automated Driving ◽  
Design Recommendations ◽  
Resonance Analysis ◽  
Performance Variability ◽  
Human Driver

Automated driving promises great possibilities in traffic safety advancement, frequently assuming that human error is the main cause of accidents, and promising a significant decrease in road accidents through automation. However, this assumption is too simplistic and does not consider potential side effects and adaptations in the socio-technical system that traffic represents. Thus, a differentiated analysis, including the understanding of road system mechanisms regarding accident development and accident avoidance, is required to avoid adverse automation surprises, which is currently lacking. This paper, therefore, argues in favour of Resilience Engineering using the functional resonance analysis method (FRAM) to reveal these mechanisms in an overtaking scenario on a rural road to compare the contributions between the human driver and potential automation, in order to derive system design recommendations. Finally, this serves to demonstrate how FRAM can be used for a systemic function allocation for the driving task between humans and automation. Thus, an in-depth FRAM model was developed for both agents based on document knowledge elicitation and observations and interviews in a driving simulator, which was validated by a focus group with peers. Further, the performance variabilities were identified by structured interviews with human drivers as well as automation experts and observations in the driving simulator. Then, the aggregation and propagation of variability were analysed focusing on the interaction and complexity in the system by a semi-quantitative approach combined with a Space-Time/Agency framework. Finally, design recommendations for managing performance variability were proposed in order to enhance system safety. The outcomes show that the current automation strategy should focus on adaptive automation based on a human-automation collaboration, rather than full automation. In conclusion, the FRAM analysis supports decision-makers in enhancing safety enriched by the identification of non-linear and complex risks.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

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.

Development of Warning System and Driving Model for Overtaking Trucks on Two-Lane Highways

2019 ◽  
Vol 2673 (11) ◽  
pp. 1-12 ◽  
Author(s):  
Udai Hassein ◽  
Maksym Diachuk ◽  
Said Easa
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Driving Simulator ◽  
Real Life ◽  
Warning System ◽  
Steering Control ◽  
Collision Warning ◽  
Radar Sensors ◽  
Sight Distance ◽  
The Impact

Passing collisions are one of the most serious traffic safety problems on two-lane highways. These collisions occur when a driver overestimates the available sight distance. This paper presents a framework for a passing collision warning system (PCWS) that assists drivers in avoiding passing collisions by reducing the likelihood of human error. The system uses a combination of a camera and radar sensors to identify the impeding vehicle type and to detect the opposing vehicles traveling in the left lane. The study involved the development of a steering control model providing lane-change maneuvers, the design of a driving simulator experiment that allows for the collection of data necessary to estimate passing parameters, and the elaboration of the algorithm for the PCWS based on sensor signals to detect impeding vehicles such as trucks. Simulation tests were carried out to confirm the effectiveness of the proposed PCWS algorithm. The impact of driver behavior on passing maneuvers was also investigated. Mathematical and imitation models were enhanced to implement Simulink for replications of real-life driving scenarios. The different factors that affect system accuracy were also examined.

scholarly journals Analysis of the advancements in real-life performance of highly automated vehicles' with regard to the road traffic safety

2018 ◽  
Vol 231 ◽  
pp. 05003 ◽  
Author(s):  
Arkadiusz Matysiak ◽  
Paula Razin
Keyword(s):  
Traffic Safety ◽  
Large Scale ◽  
Road Traffic ◽  
Real Life ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The Road ◽  
Road Traffic Safety ◽  
On The Road ◽  
The Impact

The article presents the analysis of the performance of the vehicles equipped with automated driving systems (ADS) which were tested in real-life road conditions from 2015 to 2017 in the state of California. It aims at the effort to assess the impact on the road safety the continuous technological advancements in driving automation might have, based on of the first large-scale, real-life test deployments. Vehicle manufacturers and other stakeholders testing the highly automated vehicles in California are obliged to issue yearly reports which provide an insight on the test scale as well as the technology maturity. The so-called 'disengagement reports' highlight the range and number of control takeovers between the ADS and driver, which are made either based on driver's decision or information provided by the vehicle itself. The analysis of these reports allowed to investigate the development made in automated driving technology throughout the years of tests, as well as the direct or indirect influence of the external factors (e.g. various weather conditions) on the ADS performance. The results show that there is still a significant gap in reliability and safety between human drivers and highly automated vehicles which has been yet steadily decreasing due to technology advancements made while driving in the specific infrastructure and traffic conditions of California.

scholarly journals Research on the classification for road traffic visibility based on the characteristics of driving behaviour – a driving simulator experiment

2020 ◽  
Vol 3 (1) ◽  
pp. 30-36
Author(s):  
Kun Wang ◽  
Weihua Zhang ◽  
Zhongxiang Feng ◽  
Cheng Wang
Keyword(s):  
Traffic Safety ◽  
Driving Simulator ◽  
Road Traffic ◽  
Driving Behavior ◽  
Control Measures ◽  
Content Type ◽  
The Road ◽  
Road Traffic Safety ◽  
Fine Classification

Purpose The purpose of this paper is to perform fine classification of road traffic visibility based on the characteristics of driving behavior under different visibility conditions. Design/methodology/approach A driving simulator experiment was conducted to collect data of speed and lane position. ANOVA was used to explore the difference in driving behavior under different visibility conditions. Findings The results show that only average speed is significantly different under different visibility conditions. With the visibility reducing, the average vehicle speed decreases. The road visibility conditions in a straight segment can be divided into five levels: less than 20, 20-30, 35-60, 60-140 and more than 140 m. The road visibility conditions in a curve segment can be also divided into four levels: less than 20, 20-30, 35-60 and more than 60 m. Originality/value A fine classification of road traffic visibility has been performed, and these classifications help to establish more accurate control measures to ensure road traffic safety under low-visibility conditions.

scholarly journals Development of framework for in-vehicle collision warning system considering driver characteristics

2021 ◽  
Author(s):  
Udai Hassein
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Field Experiments ◽  
Driving Simulator ◽  
Warning System ◽  
Design Guidelines ◽  
Initial Time ◽  
Data Logger ◽  
Collision Warning ◽  
Acceleration Rate

Passing collisions are one of the most serious traffic safety problems on two-lane highways. The purpose of this dissertation is to develop a framework for a passing collision warning system (PCWS) for two-lane highways that can help drivers avoid passing collisions by reducing the chance of human error. Specifically, the objectives of this research are: (1) to conduct a comprehensive literature review of existing collision warning systems, (2) to design driving simulator and field experiments for data collection, (3) to develop deterministic and reliability-based models for passing sight distance (PSD) that consider driver characteristics, (4) to develop an in-vehicle passing collision warning system, and (5) to develop a Simulink model that implements the proposed PCWS in a real time environment. A driving simulator was used to collect data from participants (males and females of different ages). The field study data werecollected on real highways using an in-vehicle video camera and a GPS data logger. The driving simulator and field data were used to develop the passing parameters for the proposed PSD model. The estimated parameters included initial time, passing time, and average acceleration rate. The results of the model were compared with those from existing models and design guidelines. The results revealed that the existing PSD models were either too liberal or too conservative. The reliability-based PSD model was developed using the First-Order Second-Moments method and a Monte Carlo Simulation was used to validate the model. The proposed PCWS uses a radar sensor placed in the passing vehicle to detect opposing vehicles travelling in the left lane and calculate their relative distance and speed in order to estimate the time to collision. This time is then compared with the time required for the passing vehicle to clear the path. The “safe pass” signal can assist passing drivers in preparing for a safe passing maneuver during the overtaking process. A Simulink MATLAB model was developed and used to implement the methodology of the proposed warning system. The different factors that affect system accuracy were examined. The application of the system was illustrated using an example.

The influence of experienced severe road traffic accidents on take-over reactions and non-driving-related tasks in an automated driving simulator study

2021 ◽  
Vol 162 ◽  
pp. 106408
Author(s):  
Klemens Weigl ◽  
Clemens Schartmüller ◽  
Philipp Wintersberger ◽  
Marco Steinhauser ◽  
Andreas Riener
Keyword(s):  
Traffic Accidents ◽  
Driving Simulator ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Automated Driving ◽  
Simulator Study

scholarly journals Does Adaptive Mode Transition Contribute to Better Driver Intervention in Highly Automated Driving?

2019 ◽  
Vol 63 (1) ◽  
pp. 287-291
Author(s):  
Huiping Zhou ◽  
Makoto Itoh ◽  
Satoshi Kitazaki
Keyword(s):  
Driving Simulator ◽  
Mode Transition ◽  
Automated Driving ◽  
Automated Control ◽  
Human Driver ◽  
Level 3 ◽  
Level Transition ◽  
Verbal Messages ◽  
Level 2 ◽  
Highly Automated Driving

This paper presents an adaptive mode (level) transition in highly combined driving automation in which the mode of a system could adaptively shift to any level including SAE level 3 (conditional automation, CA) to level 2 (partial automation) based on the driving environment. We show the effects of the adaptive transition on the take over of car control by a human driver and driving behavior after intervention when the system issues a response to intervene. A driving simulator experiment is conducted to collect data during the transition from automated control to manual driving in three scenes: obstacle on a driving lane, blurred lane mark, and stopped car ahead. Results indicate that the interventions of drivers who experience the adaptive transition are delayed in comparison to those who experience only the fixed transition. The adaptive transition is conducive for drivers to stop the car for preventing a potential collision with a stopped car ahead. Owing to the adaptive transition, drivers perceive a critical hazard after taking over car control and provide a rapid response. In addition, during the adaptive transition, drivers prefer verbal messages to the simple “beeping” message.

scholarly journals Development of framework for in-vehicle collision warning system considering driver characteristics

2021 ◽  
Author(s):  
Udai Hassein
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Field Experiments ◽  
Driving Simulator ◽  
Warning System ◽  
Design Guidelines ◽  
Initial Time ◽  
Data Logger ◽  
Collision Warning ◽  
Acceleration Rate

Passing collisions are one of the most serious traffic safety problems on two-lane highways. The purpose of this dissertation is to develop a framework for a passing collision warning system (PCWS) for two-lane highways that can help drivers avoid passing collisions by reducing the chance of human error. Specifically, the objectives of this research are: (1) to conduct a comprehensive literature review of existing collision warning systems, (2) to design driving simulator and field experiments for data collection, (3) to develop deterministic and reliability-based models for passing sight distance (PSD) that consider driver characteristics, (4) to develop an in-vehicle passing collision warning system, and (5) to develop a Simulink model that implements the proposed PCWS in a real time environment. A driving simulator was used to collect data from participants (males and females of different ages). The field study data werecollected on real highways using an in-vehicle video camera and a GPS data logger. The driving simulator and field data were used to develop the passing parameters for the proposed PSD model. The estimated parameters included initial time, passing time, and average acceleration rate. The results of the model were compared with those from existing models and design guidelines. The results revealed that the existing PSD models were either too liberal or too conservative. The reliability-based PSD model was developed using the First-Order Second-Moments method and a Monte Carlo Simulation was used to validate the model. The proposed PCWS uses a radar sensor placed in the passing vehicle to detect opposing vehicles travelling in the left lane and calculate their relative distance and speed in order to estimate the time to collision. This time is then compared with the time required for the passing vehicle to clear the path. The “safe pass” signal can assist passing drivers in preparing for a safe passing maneuver during the overtaking process. A Simulink MATLAB model was developed and used to implement the methodology of the proposed warning system. The different factors that affect system accuracy were examined. The application of the system was illustrated using an example.

scholarly journals Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

2020 ◽  
Vol 12 (9) ◽  
pp. 3799 ◽  
Author(s):  
Benedikt Schwab ◽  
Christof Beil ◽  
Thomas H. Kolbe
Keyword(s):  
Driving Simulator ◽  
Test Procedure ◽  
Road User ◽  
Automated Driving ◽  
Test Drive ◽  
Human Driver ◽  
Space Modeling ◽  
Road Space ◽  
Environment Simulation ◽  
System Functioning

Automated driving technologies offer the opportunity to substantially reduce the number of road accidents and fatalities. This requires the development of systems that can handle traffic scenarios more reliable than the human driver. The extreme number of traffic scenarios, though, causes enormous challenges in testing and proving the correct system functioning. Due to its efficiency and reproducibility, the test procedure will involve environment simulations to which the system under test is exposed. A combination of traffic, driving and Vulnerable Road User (VRU) simulation is therefore required for a holistic environment simulation. Since these simulators have different requirements and support various formats, a concept for integrated spatio-semantic road space modeling is proposed in this paper. For this purpose, the established standard OpenDRIVE, which describes road networks with their topology for submicroscopic driving simulation and HD maps, is combined with the internationally used semantic 3D city model standard CityGML. Both standards complement each other, and their combination opens the potentials of both application domains—automotive and 3D GIS. As a result, existing HD maps can now be used by model processing tools, enabling their transformation to the target formats of the respective simulators. Based on this, we demonstrate a distributed environment simulation with the submicroscopic driving simulator Virtual Test Drive and the pedestrian simulator MomenTUM at a sensitive crossing in the city of Ingolstadt. Both simulators are coupled at runtime and the architecture supports the integration of automated driving functions.

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