Frederik Naujoks

BMW Group

Highly automated driving may improve driving comfort and safety in the near future. Due to possible system limits of highly automated driver support, the driver is expected to take over the vehicle control, if a so-called take-over request is issued. One example of these system limits are missing or ending lines on motorways. This study focuses on...

Different motor vehicle manufacturers have recently introduced assistance systems that are capable of both longitudinal and lateral vehicle control, while the driver still has to be able to take over the vehicle control at all times (so-called Partial Automation). While these systems usually allow hands-free driving only for short time periods (e.g...

Up to a level of full vehicle automation, drivers will have to be available as a fallback level and take back manual control of the vehicle in case of system limits or failures. Before introducing automated vehicles to the consumer market, the controllability of these control transitions has to be demonstrated. This paper presents a novel procedure...

Automated driving systems are getting pushed into the consumer market, with varying degrees of automation. Most often the driver's task will consist of being available as a fall-back level when the automation reaches its limits. These so-called takeover situations have attracted a great body of research, focusing on various human factors aspects (e...

The present study investigated the effects of a driver monitoring system that triggers attention warnings in case distraction is detected. Based on the EuroNCAP protocol, distraction could either be long distraction (>3s) or visual attention time sharing (>10 cumulative seconds within a 30 second time interval). In a series of driving simulator dri...

(57) Zusammenfassung: Die Erfindung betrifft ein Hinweisgerät (22) zur Nutzung in einem bedingt automatisierten Kraftfahrzeug (12). Das Hinweisgerät (22) weist einen Empfänger zum Empfangen eines leitungslos gesendeten Signals (26), eine Halteeinrichtung (24) zum Halten des Hinweisgeräts (22) an einem Fremdobjekt (20) und eine Anzeigeeinrichtung (2...

The Box Task combined with a Detection Response Task (BT + DRT) is a relatively new and easy-to-use method to assess in-vehicle system demand, consisting of a visual-manual task (BT) and a cognitive task (tactile DRT). Currently, little is known regarding the sensitivity of the BT + DRT for different types and difficulty levels of secondary tasks....

(57) Zusammenfassung: Es wird eine Vorrichtung (101) zur Erhöhung der Aufmerksamkeit eines Fahrers eines Fahrzeugs (100) beschrieben. Die Vorrichtung (101) ist eingerichtet, einen Aufmerksamkeitsindikator in Bezug auf die Aufmerksamkeit des Fahrers für eine Fahraufgabe zu ermitteln. Die Vorrichtung (101) ist ferner eingerichtet, ein im Fahrzeug (10...

Speech-based interfaces can be a promising alternative and/or addition to visual-manual interfaces since they reduce visual-manual distraction while driving. However, there are also findings indicating that speech-based assistants may be a source of cognitive distraction. The aim of this experiment was to quantify drivers' cogni-tive distraction wh...

Augmented reality (AR) technology could establish direct relationships between displayed information and objects in the real driving environment, e.g. by highlighting relevant objects in the traffic environment. However, it is unclear how these potential benefits of augmentation affect drivers’ distraction from the driving task and their level of w...

There are several standardized test methods to assess the distraction potential of secondary task engagement while driving. One relatively new method is the Box Task combined with a Detection Response Task (BT + DRT). This method has the potential to distinguish between different dimensions of driver distraction. While the DRT is being implemented...

The Box Task (BT) combined with a Detection Response Task (DRT) is a method to assess different dimensions of secondary task demand caused by portable (electronic) devices or in-vehicle systems. This paper presents a comprehensive analysis of the DRT using an evidence accumulation model. The aim was to replicate influences of cognitive load on the...

The presented simulator study investigated the effectiveness, user experience and usability of an innovative driver monitoring system (DMS) for partially automated driving, called "Jeannie". This virtual assistant provided continuous visual emotional feedback dependent on drivers' monitoring behaviour and issued warnings and speech outputs in respo...

Speech is considered a promising modality for human-machine interaction while driving, especially in reducing visual and manual distraction. However, speech-based user interfaces themselves have shown to increase cognitive distraction. There remains a lack of standardized and unambiguous methods for measuring the impact of speech-based assistants o...

Bidirectional charging management could offer benefits to individuals, society and energy providers by using the batteries of Battery Electric Vehicles (BEVs) as a means of storage. To successfully design and implement this technology, it is necessary to match customer mobility needs with the goals of the energy sector. Therefore, practice and rese...

Driving automation systems have already entered the commercial market and further advancements will be introduced in the near future. Level 3 automated driving systems are expected to increase safety, comfort and traffic efficiency. For the human driver, these functions and according human-machine interfaces are a novel technology. In the human fac...

The assessment of task demand caused by in-vehicle systems is crucial to avoid distraction while driving. The Box Task (BT) in combination with a tactile Detection Response Task (DRT) provides a method for measuring both visual-manual and cognitive secondary task demand. In the present study, the impact of cognitive, auditory-verbal tasks on the BT...

Conditionally automated driving (L3) implies repeated transitions of the driving responsibility between the human operator and the automated driving system. This research examines users’ attitudes towards speech outputs as potential features for human-machine interfaces for L3 automated driving. The Kano method is applied to identify scenarios wher...

The System Usability Scale (SUS) is a widely used questionnaire to assess the subjective usability of interactive products or services. It is a cost-effective and time-efficient method which makes it a convenient instrument in the industry context worldwide. Past research has already demonstrated psychometric reliability and validity of the SUS in...

The Code of Practice (ITS World Congress 2021 revision) for the Development of Automated Driving Functions (CoP-ADF) is one of the major achievements of the EU-funded automotive research project L3Pilot running from 2017 to 2021. It provides comprehensive guidelines for supporting the design, development, verification and validation of automated dr...

Empirical validation and verification procedures require the sophisticated development of research methodology. Therefore, researchers and practitioners in human-machine interaction and the automotive domain have developed standardized test protocols for user studies. These protocols are used to evaluate human-machine interfaces (HMI) for driver di...

In-vehicle information systems allow drivers to engage in secondary tasks, such as selecting music via the infotainment system, while driving. However, interacting with such systems can lead to visual, manual as well as cognitive distraction. Assessing in-vehicle system demand while driving is, therefore, a central topic in driver distraction resea...

Intelligent Personal Assistants (IPAs) have grown into technologically mature systems. However, instruments used for evaluating the usability and user experience of IPAs were developed two decades ago. This bears the danger for research and development to apply inadequate measurements to a novel technology. In this study, more recent scales from hu...

The introduction of conditionally automated driving [25] implies repeated transitions of the driving task between the human operator and the automated driving system (ADS). Human-machine interfaces (HMIs) facilitating these shifts in control are essential. Usability serves as an important criterion to assess the quality of an HMI design. This paper...

The European research project L3Pilot combines different activities, one of the most important is to report on the Code of Practice for the development of Automated Driving Functions (CoP-ADF), which is the main objective of this document. The CoP-ADF shall provide a comprehensive guideline for supporting the automotive industry and relevant stakeh...

The use of advanced in-vehicle information systems (IVIS) and other complex devices such as smartphones while driving can lead to driver distraction, which, in turn, increases safety-critical event risk. Therefore, using methods for measuring driver distraction caused by IVIS is crucial when developing new in-vehicle systems. In this paper, we pres...

For a successful market introduction of Level 3 Automated Driving Systems (L3 ADS), a careful evaluation of human–machine interfaces (HMIs) is necessary. User preference has often focused on usability, user experience, acceptance and trust. However, a thorough evaluation of measures when applied to ADS HMIs is missing. We investigated the appropria...

The human-machine interface of automated driving systems (ADS) will play a crucial role in their safe, comfortable and efficient use. For example, the ADS HMI should be capable of efficiently informing the user about the current automated driving mode and the user’s responsibilities (e.g., whether the ADS is functioning properly or requesting a tra...

Several tools have been developed over the past twenty years to assess the degree of driver distraction caused by secondary task engagement. A relatively new and promising method in this area is the box task combined with a detection response task (BT + DRT). However, no evaluation regarding the BT's sensitivity currently exists. Thus, the aim of t...

Previous research on external Human-Machine Interfaces (eHMI) has primarily focused on interactions between automated vehicles and pedestrians. So far, little attention has been paid to the cyclist as vulnerable interaction partner. Compared to pedestrians, interactions with cyclists are usually much more dynamic and might, therefore, lead to diffe...

Today, OEMs and suppliers can rely on commonly agreed and standardized test and evaluation methods for in-vehicle human-machine interfaces (HMIs). These have traditionally focused on the context of manually driven vehicles and put the evaluation of minimizing distraction effects and enhancing usability at their core (e.g., AAM guidelines or NHTSA v...

We examined the necessity for plausibilization of test scenarios within usability studies for AV HMIs in driving simulator studies. One group of drivers experienced system-initiated transitions without any obvious reason, the other with plausible reasons (e.g. fog for L3 → L2 transition, broken-down vehicle for L3 TOR). The results showed that reac...

To ensure safe interactions between automated vehicles and non-automated road users in mixed traffic environments, recent studies have focused on external human-machine interfaces (eHMI) as a communication interface of automated vehicles. Most studies focused on the research question which kind of eHMI can support this interaction. However, the fun...

Partially automated driving (PAD, Society of Automotive Engineers (SAE) level 2) features provide steering and brake/acceleration support, while the driver must constantly supervise the support feature and intervene if needed to maintain safety. PAD could potentially increase comfort, road safety, and traffic efficiency. As during manual driving, u...

The evaluation of the distraction potential of secondary task activities while driving has traditionally been focused on visual-manual tasks. In previous years, different test protocols have been developed and standardized to evaluate the distraction effects of in-vehicle information systems while driving. However, the assessment of cognitive distr...

The advancement of SAE Level 3 automated driving systems requires best practices to guide the development process. In the past, the Code of Practice for the Design and Evaluation of ADAS served this role for SAE Level 1 and 2 systems. The challenges of Level 3 automation make it necessary to create a new Code of Practice for automated driving (CoP-...

The projected introduction of conditional automated driving systems to the market has sparked multifaceted research on human-machine interfaces (HMIs) for such systems. By moderating the roles of the human driver and the driving automation system, the HMI is indispensable in avoiding side effects of automation such as mode confusion, misuse, and di...

Research on the role of non-driving related tasks (NDRT) in the area of automated driving is indispensable. At the same time, the construct mode awareness has received considerable interest in regard to human–machine interface (HMI) evaluation. Based on the expectation that HMI design and practice with different levels of driving automation influen...

Within a workshop on evaluation methods for automated vehicles (AVs) at the Driving Assessment 2019 symposium in Santa Fe; New Mexico, a heuristic evaluation methodology that aims at supporting the development of human–machine interfaces (HMIs) for AVs was presented. The goal of the workshop was to bring together members of the human factors commun...

L3Pilot (www.l3pilot.eu) published its proposal for a Code-of-Practice for the development and validation of highly Automated Driving Functions (SAE Levels >=3) as deliverable D2.2. L3Pilot combines different activities. The main objective of this deliverable is to report on the draft version of the Code of Practice for automated driving (CoP-AD)....

Research on external human–machine interfaces (eHMIs) has recently become a major area of interest in the field of human factors research on automated driving. The broad variety of methodological approaches renders the current state of research inconclusive and comparisons between interface designs impossible. To date, there are no standardized tes...

Problem: Some evidence exists that drivers choose to engage in secondary tasks when the driving demand is low (e.g., when the car is stopped). While such a behavior might generally be considered as rather safe, it could be argued that the associated diversion of attention away from the road still leads to a reduction of situational awareness, whic...

The driving task is becoming increasingly automated, thus changing the driver's role. Moreover, in-vehicle information systems using different display positions and information processing channels might encourage secondary task engagement. During manual driving scenarios, varying secondary tasks and display positions could influence driver's glance...

The success of introducing automated driving systems to consumers will depend on an appropriate understanding and human-automation interaction with this technology. Educating users on driving automation technology bears the potential to attain these two requirements. In a driving simulator study, we investigated the effects of user education on men...

Conditional automated driving (CAD) systems (SAE level 3) will soon be introduced to the public market. This automation level is designed to take care of all aspects of the dynamic driving task in specific application areas and does not require the driver to continuously monitor the system performance. However, in contrast to higher levels of autom...

Objective: The human–machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that—depending on the operational design domain (ODD)—different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator’s responsibiliti...

Higher level cognitive processes such as learning and mental models play a fundamental role in the success of automated driving, as technology can only be as good as our understanding and expectations of it. The present study investigated the development of these processes during interactions with driving automation. In a driving simulator study, N...

With the Federal Automated Vehicles Policy, the U.S. National Highway Traffic Safety Administration (NHTSA) has provided an outline that can be used to guide the development and validation of Automated Driving Systems (ADS). Acknowledging that the Human-Machine-Interface (HMI) – identified as one of the 12 priority safety design elements in this vo...

For a successful market introduction of Level 3 Automated Driving Systems (L3 ADS), a careful evaluation of Human-Machine Interfaces (HMIs) is necessary. This paper describes an empirical evaluation of a checklist that has been previously developed for the use in heuristic expert assessments, demonstrating that an ADS HMI that meets the guidelines...

The development of automated driving will profit from an agreed-upon methodology to evaluate human-machine interfaces. The present study examines the role of feedback on interaction performance provided directly to participants when interacting with driving automation (i.e., perceived ease of use). In addition, the development of ratings itself ove...

Automated driving systems (ADS) and a combination of these with advanced driver assistance systems (ADAS) will soon be available to a large consumer population. Apart from testing automated driving features and human-machine interfaces (HMI), the development and evaluation of training for interacting with driving automation has been largely neglect...

To evaluate human-machine interfaces for automated driving systems, a robust methodology is indispensable. The present driving simulator study investigated the effect of practice on behavioral measures (i.e., experimenter rating, reaction times, error rate) and the development of the preference-performance relationship for automated driving human-m...

Objective: This study aimed at investigating the driver’s takeover performance when switching from working on different non–driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions. Background:...

The introduction of conditionally automated driving (CAD) will change the role of the driver fundamentally. Drivers can be engrossed in non-driving related tasks (NDRTs) or fulfill a new role of a passive passenger while the automation executes the dynamic driving task (DDT). In both cases, fatigue and/or drowsiness could impair drivers’ availabili...

In most levels of vehicle automation, drivers will not be merely occupants or passengers of automated vehicles. Especially in lower levels of automation, where the driver is still required to serve as a fallback level (SAE L3) or even as a supervisor (SAE L2), there is a need to communicate relevant system states (e.g., that the automated driving s...

Mobile phone related task engagement while driving has increased dramatically over the past years. However, research has shown that drivers attempt to compensate for the associated performance degradation in the primary driving task by using various self-regulatory strategies, such as deciding when to engage in a secondary task. Unfortunately, ther...

Introduction Within the next years, vehicles will be capable of taking over the driving task in certain environments without the need to be continuously monitored by the user. This so-called conditionally automated driving (L3-automation according to SAE J3016 [1]), unlike highly or fully automated driving, still will require the user as a fallbac...

Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be gen...

An automated vehicle needs to learn how human road users communicate with each other in order to avoid misunderstandings and prevent giving a negative outward image during interactions. The aim of the present work is to develop an autonomous driving system which communicates its intentions to change lanes based on implicit and explicit rules used b...

It will not be long until Level 3 Automated Driving Systems (L3 ADS) enter the consumer market. An important role corresponds to methodology development. The present paper gives impetus to the process of developing robust methods for evaluating Human-Machine Interfaces (HMI) for L3 ADS. First, a literature review on automotive interfaces concerning...

This paper investigates whether an Augmented Reality Head-up Display (AR-HUD) supports usability and reduces visual demand during conditionally automated driving. In a driving simulator study, 24 drivers experienced several driving scenarios while driving with conditional automation. The drivers completed one drive with a fully developed HMI design...

This work investigated differences between preference and performance in Human Computer Interactions and their dependency to the respective user skill level. A driving simulator study with N=57 participants was conducted to evaluate a Human-Machine Interface for a Level 3 Automated Driving System. Two experimenters rated interaction performance (e....

Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be gen...

Background: Until the level of full vehicle automation is reached, users of vehicle automation systems will be required to take over manual control of the vehicle occasionally and stay fallback-ready to some extent during the drive. Both, drowsiness caused by inactivity and the engagement in distracting non-driving related tasks (NDRTs) such as ent...