Test Drivers Research Articles

Exploring the circular last mile: reusable packaging in home delivery of food and groceries using a design science approach

ABSTRACT The market for home deliveries of food and groceries has been growing in the last years, and with it so have the associated environmental impacts: emissions from the delivery routes, energy for the cold chain and packaging waste are some of the main concerns. The purpose of this paper is to contribute with knowledge to support the development and implementation of circular retail supply chains, with focus on customer involvement and logistics operations in home deliveries of food and groceries. This study is based on a design science approach that follows three main steps: (1) development of the ‘circular delivery concept’ (i.e., the artefact), (2) field-test in real-life conditions and (3) key-learnings and implications on theoretical, managerial, and policy domains. The feedback from the actors involved in the concept (customers, production/warehouse staff, delivery drivers and e-tailer management) was collected through semi-structured interviews, observations, and workshops. Results show potential of the circular delivery concept at test, with customers and delivery drivers recognising positive aspects and a good potential from the new setting. The warehouse staff and the management board, were, however, sceptical of the concept and expressed concerns on the practicability to scale it up because of the challenges in operationalising it in the warehouse. The main barriers found in this study relate to the need to implement new processes for circular supply chains in efficient and cost-effective ways, and to get customers more actively involved and to assume their responsibilities. For the latter, the added value needs to be conveyed more clearly and pragmatically so that the customer recognises its value as obvious and clear. Managerial and policy implications were deduced, as well as theoretical implications that contribute to the theory of consumption value.

Effect of A-Pillar Blind Spots on a Driver's Pedestrian Visibility during Vehicle Turns at an Intersection.

This study aims to elucidate the impact of A-pillar blind spots on drivers' visibility of pedestrians during left and right turns at an intersection. An experiment was conducted using a sedan and a truck, with a professional test driver participating. The driver was instructed to maintain sole focus on a designated pedestrian model from the moment it was first sighted during each drive. The experimental results revealed how the blind spots caused by A-pillars occur and clarified the relationship between the pedestrian visible trajectory distance and specific vehicle windows. The results indicated that the shortest trajectory distance over which a pedestrian remained visible in the sedan was 17.6 m for a far-side pedestrian model during a right turn, where visibility was exclusively through the windshield. For the truck, this distance was 20.9 m for a near-side pedestrian model during a left turn, with visibility through the windshield of 9.5 m (45.5% of 20.9 m) and through the passenger-side window of 11.4 m (54.5% of 20.9 m). Additionally, we quantified the trajectory distances where pedestrians became invisible when the driver's view was obstructed by A-pillars. The sedan exhibited the highest invisibility rate at 46.1% for a far-side pedestrian model during a right turn, followed by the truck at 17.8% for the same model. These findings will be instrumental in developing new driving support systems aimed at enhancing visibility in situations where pedestrians are obscured by A-pillars.

A Predictive Quality Inspection Framework for the Manufacturing Process in the Context of Industry 4.0.

The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancing production efficiency, reducing costs, and shortening the manufacturing time of automobiles. The principal results demonstrate that the predictive quality inspection framework significantly improves defect detection and supports personalized road tests. The major conclusions indicate that integrating AI into quality control processes offers a sustainable, long-term solution for continuous improvement in automotive manufacturing, ultimately increasing overall production efficiency. The economic benefit of our solution is significant. Currently, a final test drive takes 10-30 min, depending on the car model. If 200,000-300,000 cars are produced annually and our data prediction of quality saves 10 percent of test drives with test drivers, this represents a minimum annual saving of 200,000 production minutes.

Testing driver warning systems for off-road industrial vehicles using a cyber-physical simulator

ADAS (Advanced Driver Assistance Systems) are becoming increasingly popular in on-road vehicles due to their safety, productivity, and cost savings. In the same way, off-road vehicles can benefit from ADAS systems to improve the security of drivers and workers in industrial settings. In this work, we study, design, and develop a novel security system to be integrated into industrial vehicles. This system is built to provide one-way Human Computer Interaction, from the computer to the human, so providing, through the interaction with the ADAS system, feedback to drivers about their surroundings, such as nearby workers, and thus helping to avoid collisions and prevent incidents. The study evaluates the quality of different feedback mechanisms, with the goal of designing the ADAS that produces the best User eXperience (UX). These feedback mechanisms are generated by LEDs in different display formats and colors, as well as with haptic feedback. We created a hybrid testbed using a realistic ADAS and a forklift simulator, integrating the system into a physical structure that resembles an industrial vehicle (a forklift) and used a computer-based simulation of a warehouse to gather the information from users. We performed a study with 36 people for the evaluation of the different feedback mechanisms tested, evaluating the results both from an objective point of view based on the results of the simulation, and a subjective point of view through a questionnaire and the stress of the users in each test.

Spatio-temporal dynamics and drivers of highly pathogenic avian influenza H5N1 in Chile.

Highly pathogenic avian influenza A H5N1 clade 2.3.4.4b (hereafter H5N1) is causing vast impacts on biodiversity and poultry around the globe. In Chile, lethal H5N1 cases have been reported in a wide range of wild bird species, marine mammals, backyard and industrial poultry, and humans. This study describes the spatio-temporal patterns of the current epizootic of H5N1 in Chile and test drivers that could be associated with outbreak occurrence. We used H5N1 cases reported by the Chilean National Animal Health Authority from 5 December 2022 to 5 April 2023. These included wild bird cases confirmed through an avian influenza-specific real-time reverse transcription PCR assay (RT-qPCR), obtained from passive and active surveillance. Data were analyzed to detect the presence of H5N1 clusters under space-time permutation probability modeling, the association of H5N1 with distance and days since the first outbreak through linear regression, and the correlation of H5N1 presence with a number of ecological and anthropogenic variables using general linear modeling. From 445 H5N1 identified outbreaks involving 613 individual cases in wild birds, a consistent wave-like spread of H5N1 from north to south was identified, which may help predict hotspots of outbreak risk. For instance, seven statistically significant clusters were identified in central and northern Chile, where poultry production and wildlife mortality are concentrated. The presence of outbreaks was correlated with landscape-scale variables, notably temperature range, bird richness, and human footprint. In less than a year, H5N1 has been associated with the unusual mortality of >100,000 individuals of wild animals in Chile, mainly coastal birds and marine mammals. It is urgent that scientists, the poultry sector, local communities, and national health authorities co-design and implement science-based measures from a One Health perspective to avoid further H5N1 spillover from wildlife to domestic animals and humans, including rapid removal and proper disposal of wild dead animals and the closure of public areas (e.g., beaches) reporting high wildlife mortalities.

Diaphragm-Induced Attenuation in Shock Tubes

This is a numerical and experimental study on shock attenuation in shock tubes. Tube geometry, boundary layer, and reduction in cross-sectional area induced by burst diaphragms are often considered the main causes of shock speed reduction (or shock attenuation) observed during the experiments. In order to distinguish how each single phenomenon contributes toward shock attenuation, the National Cheng Kung University shock tube is simulated for driver and test (driver–test) gas pairs involving air–air and He–air. For low pressure ratios, the diaphragm effect was found to be negligible. For a pressure ratio of 200 and helium as driver gas, the shock speed was found to decrease linearly with the size of the diaphragm orifice. In general, wall viscous effects caused a 2.5% decrease in shock speed, while an additional 7% reduction was caused by the presence of the diaphragm. The gradual opening of the diaphragm mainly contributed to a decrease in shock oscillations.

Real-time multibody simulation of vehicle wheel suspensions of different topologies with elastokinematic properties

AbstractDriving simulators are becoming increasingly important in vehicle development, as they allow test drivers to evaluate the driving characteristics of a vehicle at an early stage of the development process. Vehicle dynamics models used for such real-time applications are usually simplified to achieve sufficiently low computation times, especially with respect to the elastokinematic properties of the wheel suspension. This contribution introduces a real-time capable multibody simulation model for wheel suspensions taking into account their elastokinematics. The underlying data model allows the parametrization of different suspension topologies. Linear-implicit integration methods enable real-time capable integration of the underlying numerically stiff equation of motion without major model simplifications. It is shown how the computational effort of linear-implicit integration can be reduced by explicitly taking the suspension topology into account. A versatile process for the analytical online-linearization of the equation of motion of arbitrary suspension topologies is presented. Both a double wishbone and a multilink suspension are simulated with very high accuracy in real-time on a standard PC.

How do active road users act around autonomous vehicles? An inverse reinforcement learning approach

The inevitable impact of autonomous vehicles (AV) on traffic safety is becoming a reality with the progressive deployment of these vehicles in different parts of the world. Still, many questions linger in the minds of road users that will share the road and interact with these AVs on a daily basis. To answer some of these questions, this study utilized recently collected real-world AV data from the United States, with the focus mainly targeting active road users. Specifically, the 1,492 h of recorded trips were processed to extract AV-pedestrian and AV-cyclist interactions of different movement types. The interactions were then investigated to gain a better understanding of the active road users’ behavior, while excluding any interactions that involved intervention from the AVs’ human test drivers. Through deep maximum entropy inverse reinforcement learning (DME-IRL), the reward functions describing the utility of active road users were retrieved and assessed for five interaction scenarios, including parallel, opposing, crossing, and turning (left and right) interactions. In addition, the policies developed as part of the solution were used to simulate the behavior of active road users and validate the resulting conflicts in terms of safety and evasive actions. Overall, the utilized approach demonstrated high accuracy in mimicking the interaction behavior of active road users when encountering an AV, with 81–84% accuracy in predicting evasive actions in parallel and opposing interactions and 12–17% mean absolute error for safety indicators in crossing and turning interactions. In addition, the resulting reward functions provided reliable insight onto the preferences and considerations of the active road users in these situations. Overall, cyclists tend to be less cautious around AVs compared to pedestrians, while the AVs tend to slow down and leave sufficient distance from all active road users in most cases. Still, the robotic behavior of the AVs, which can sometimes be inconsistent, leads to risky evasive actions by the active road users, which can affect other road users in busy intersections.

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences.

To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned with the driver's preference is consistent with this driver's naturalistic driving characteristic. Nevertheless, this assumption may not always hold true, causing limitations to the effectiveness of this method. This paper proposes a novel method for a Driver-Adaptive Lane-Keeping Assistance (DALKA) system based on drivers' real preferences. First, metrics are extracted from collected naturalistic driving data using action point theory to describe drivers' naturalistic driving characteristics. Then, the subjective and objective evaluation method is introduced to obtain the real preference of each test driver for the LKA system. Finally, machine learning methods are employed to train a model that relates naturalistic driving characteristics to the drivers' real preferences, and the model-predicted preferences are integrated into the DALKA system. The developed DALKA system is then subjectively evaluated by the drivers. The results show that our DALKA system, developed using this method, can enhance or maintain the subjective evaluations of the LKA system for most drivers.

Enhancing Test Driver Response Skills through Accident Scenario Analysis in Real-World Evaluation of Autonomous Vehicles

Enhancing Test Driver Response Skills through Accident Scenario Analysis in Real-World Evaluation of Autonomous Vehicles

Advancements in Mixed Reality for Autonomous Vehicle Testing and Advanced Driver Assistance Systems: A Survey

Advancements in Mixed Reality for Autonomous Vehicle Testing and Advanced Driver Assistance Systems: A Survey

Commissioning, Performance, and Effect of the Quench Current-Boosting Device on a Dedicated Superconducting Magnet

Superconducting magnet training is one of the accelerator related issues attracting attention due to significant operational costs and time budget associated to it. It is especially worrisome that magnets based on the “next-generation” Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn technology are affected by long training. While various efforts are underway to better understand and resolve the problem a parallel path could also be investigated, a path bypassing the issue. Following the concept of fast induced over-current during magnet powering, FNAL has developed an upgradable capacitor-based device to discharge through a superconducting magnet at quench detection or operator chosen time. The 0.4 F/1 kV device has been tested on a 1-m-long dipole-coil in a “mirror” magnet configuration and conclusive results on magnet training elimination have been observed. In this paper we discuss the main characteristics of the device, compare simulated response and actual performance, elaborate on test drivers and outcomes. Next steps and perspectives for future use are debated.

How to Induce Drowsiness When Testing Driver Drowsiness and Attention Warning (DDAW) Systems

Driver drowsiness and attention warning systems are mandatory for new vehicles in the EU by 2022. Requirements for the test procedure for type approval are laid down in the Annex of EU regulation C/2021/2639. The aim of the presented study was to establish a robust test procedure that enables the collection of drowsy driving data in real traffic while accounting for safety and study ethics. Compared to other circadian timings, most participants reached the required drowsiness threshold when starting the test drive at 1 p.m. The drowsiness threshold of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 7 defined by the EU regulation was considered suitable.

Modeling driver behavior in critical traffic scenarios for the safety assessment of automated driving

Objective Before market introduction, the safety of highly automated driving systems needs to be assessed prospectively. BMW has developed a holistic approach for the assessment of the traffic safety impact by these systems in which stochastic traffic simulations play a significant role. A driver behavior model which represents realistic driver behavior ranging from performance in non-critical everyday driving toward performance in critical situations is key for this approach. To ensure trustworthy results, validation of the driver model is needed. The paper aims at demonstrating that the presented driver model acts realistically in different critical real-world traffic scenarios. Methods BMW has been developing the Stochastic Cognitive Model (SCM) which models cognitive processes in traffic situations. These processes range from information acquisition by gaze behavior, mental representation of the environment, recognition of situations from the visual information and reaction to the situation. The driver model combines these cognitive processes with stochastic driver parameters to obtain a variation in driver behavior in simulations. Especially visual attention modeling is key to realistic traffic interactions in simulations as this is the input for the sequential cognitive processes, i.e., the recognition of situations and the reaction to the situation. Modeling of driver’s gaze behavior with SCM is thus shown in this paper. Results SCM is applied in three critical real-world traffic scenarios in which gaze behavior, brake reaction times and time-to-collisions are evaluated and compared to the real-world data. Due to the stochastic approach not only a single SCM agent but a collective of virtual SCM test drivers is assessed. Results show that SCM is capable to simulate the influence of visual inattention on collision risk. Conclusion Realistic driver behavior in simulations can be achieved by using SCM. Especially in the presented critical scenarios SCM is able to represent real-world driving behavior which is determined particularly by its gaze behavior and subsequent reaction. Driving performance varies over different SCM agents which mean that different driving behavior can be simulated with SCM as well. However, the investigation in this paper included only three real-world cases. Therefore, further critical, and additionally non-critical scenarios need to be investigated in the future.

Effects of lane-change scenarios on lane-change decision and eye movement

Improper lane-change manoeuvre can cause traffic safety issues and even lead to serious traffic collisions. Quantifying the decision behaviour and eye movements can provide a deeper understanding of lane-change manoeuvre in vehicle interaction environment. The purpose of this study was to investigate the effect of lane-change scenarios defined by gaps on lane-change decision and eye movements. Twenty-eight participants were recruited to complete a naturalistic driving experiment. Eye movements and lane-change decision duration (LDD) were recorded and analysed. Results suggested that the scanning frequency (SF) and saccade duration (SD) were the sensitive parameters to respond to lane-change scenarios. LDD was significantly affected by the scenario, SF, and SD. The increase in LDD was related to the high difficulty gap and high frequency scanning of multiple regions. These findings evaluated the driver’s decision performance in response to different lane-change environments and provided valuable information for measuring the driver’s scenario perception ability. Practitioner summary: A naturalistic driving experiment was conducted to evaluate the interaction of lane-change decision, eye movement, and lane changing gap in a lane-change task. The results reveal the sensitive eye movement parameters to lane-change scenario, which provide guidelines for driver’s perception ability test and professional driver assessment.

Performance Evaluation of a Lane Correction Module Stress Test: A Field Test of Tesla Model 3

This paper is designed to explicate and analyze data acquired from experimental field tests of a Tesla Model 3 lane correction module within the vehicle’s Autopilot Suite, a component of Tesla OS. The initial problem was discovered during a nominal drive of the Tesla Model 3, where after a random number of lane correction events, the lane correction module shuts down, issues a visual disable warning on the touchscreen, and control of the vehicle is given to the driver until the next drive. That development was considered problematic, as the driver can be caught off guard or may be medically disabled and unable to respond. During a controlled stress test, a more severe issue was discovered. After a random number of lane correction events, the lane correction module shuts down without warning, then stays activated after the test driver corrects the vehicle’s trajectory. This is considered a fatal error in the system and adds a dangerous element to an otherwise standard feature in a modern automotive vehicle. The results established that the number of events needed to trigger a fatal error without warning is unpredictable. Our results also demonstrate that the system is inconsistent.

Heavy-Vehicle Response to Crosswind: Evaluation of Driver Reactions Using a Dynamic Driving Simulator

Heavy vehicles exiting (or entering) a tunnel at high speed under a strong crosswind is a particularly critical condition since the aerodynamic load changes drastically, greatly affecting the lateral stability of the vehicle. Often, active control systems (active suspensions, active front steering, etc.) and infrastructure elements (e.g., wind fences) are proposed to reduce the induced risks. To help the design of these devices, the present paper investigates the response of the vehicle–driver system in the case of a high-sided lorry exiting a tunnel under crosswind, by using Driver-In-the-Loop simulations. The study was performed using the dynamic driving simulator of Politecnico di Milano and 28 test drivers. Vehicle and aerodynamic models have been developed to reproduce the phenomenon in a highly immersive environment. During the tests, several combinations of vehicle and wind speed were considered. The effect of vehicle loading condition (Empty and Laden) was also investigated. The performed tests allowed us to gain information about the sequence of the driver’s actions and associated delays, which may induce lane deviation or, in the worst case, rollover. It was found that lane invasion may happen for ratios of lateral aerodynamic force over vehicle weight force bigger than 0.1, while rollover could happen for ratios bigger than 0.3. Moreover, it was found that the driver’s response typically happens with a delay of ∼0.25 s with respect to the onset of the crosswind stimulus.

Prediction of Drivers' Subjective Evaluation of Vehicle Reaction Under Aerodynamic Excitations.

The objectives are to determine which quantities are important to measure to determine how drivers perceive vehicle stability, and to develop a regression model to predict which induced external disturbances drivers are able to feel. Driver experience of a vehicle's dynamic performance is important to auto manufacturers. Test engineers and test drivers perform several on-road assessments to evaluate the vehicle's dynamic performance before sign-off for production. The presence of external disturbances such as aerodynamic forces and moments play a significant role in the overall vehicle assessment. As a result, it is important to understand the relation between the subjective experience of the drivers and these external disturbances acting on the vehicle. A sequence of external yaw and roll moment disturbances of varying amplitudes and frequencies is added to a straight-line high-speed stability simulation test in a driving simulator. The tests are performed with both common and professional test drivers, and their evaluations to these external disturbances are recorded. The sampled data from these tests are used to generate the needed regression model. A model is derived for predicting which disturbances drivers can feel. It quantifies difference in sensitivity between driver types and between yaw and roll disturbances. The model shows a relationship between steering input and driver sensitivity to external disturbances in a straight-line drive. Drivers are more sensitive to yaw disturbance than roll disturbance and increased steering input lowers sensitivity. Identify the threshold above which unexpected disturbances such as aerodynamic excitations can potentially create unstable vehicle behaviour.

Psychoacoustic characteristics of different brake creep groan classes and their subjective noise annoyance in vehicle and half-axle tests

Brake creep groan is a severely annoying noise and vibration phenomenon. Especially on the Asian market, customer feedback about creep groan is common, indicating creep groan’s impact towards the quality impression of a car. Hence, treatment of these stick–slip-related creep groan phenomena is necessary. As numerous design conflicts exist for brake and axle, a complete mitigation of the phenomenon is often not possible. A reduction of creep groan’s annoyance by changing the noise’s level and characteristics is therefore typically aspired. One approach towards this goal could include the usage of psychoacoustics: This work deals with psychoacoustic characteristics of different creep groan classes. Low-frequency groan, high-frequency groan, and transition groan classes are compared regarding loudness, sharpness, roughness, fluctuation strength, and tonality. Standard statistic methods as well as machine learning approaches are applied on signals from vehicle tests and half-axle tests. Test results depict the different characteristics of each creep groan class. By mapping the results to the subjective rating of trained test drivers, the annoyance of different classes is compared. Low-frequency groan, dominated by longitudinal axle vibrations, is found to be least annoying. This low annoyance is best depicted by the psychoacoustic parameters loudness and roughness. Presented results allow an optimization of brake system design to reduce creep groan’s annoyance, leading to higher customer satisfaction and a more goal-oriented treatment of this NVH problem.

Human Models for Future Mobility

ABSTRACTThe new DLR Institute of Systems Engineering for Future Mobility (DLR SE) opened its doors at the beginning of 2022. As the new DLR institute emerged from the former OFFIS Division Transportation, it can draw on more than 30 years of experience in the research field on safety critical systems. With the transition to the German Aerospace Center (DLR), the institute has developed a new research roadmap focusing on technical trustworthiness for highly automated and autonomous systems, as described in the article “DLR Institute of Systems Engineering for Future Mobility – Technical Trustworthiness as a Basis for Highly Automated and Autonomous Systems” in this journal. In this paper, we describe how the Group Human Centered Engineering (HCE) contributes to this roadmap with our methods of “virtual test drivers” and “virtual co‐drivers.”