Modeling driver-vehicle interaction in automated driving

In automated vehicles, the collaboration of human drivers and automated systems plays a decisive role in road safety, driver comfort, and acceptance of automated vehicles. A successful interaction requires a precise interpretation and investigation of all influencing factors such as driver state, system state, and surroundings (e.g., traffic, weather). This contribution discusses the detailed structure of the driver-vehicle interaction, which takes into account the driving situation and the driver state to improve driver performance. The interaction rules are derived from a controller that is fed by the driver state within a loop. The regulation of the driver state continues until the target state is reached or the criticality of the situation is resolved. In addition, a driver model is proposed that represents the driver’s decision-making process during the interaction between driver and vehicle and during the transition of driving tasks. The model includes the sensory perception process, decision-making, and motor response. The decision-making process during the interaction deals with the cognitive and emotional states of the driver. Based on the proposed driver-vehicle interaction loop and the driver model, an experiment with 38 participants is performed in a driving simulator to investigate (1) if both emotional and cognitive states become active during the decision-making process and (2) what the temporal sequence of the processes is. Finally, the evidence gathered from the experiment is analyzed. The results are consistent with the suggested driver model in terms of the cognitive and emotional state of the driver during the mode change from automated system to the human driver.In automatisierten Fahrzeugen spielt die Zusammenarbeit vom menschlichen Fahrer und automatisierten Systemen eine entscheidende Rolle für die Verkehrssicherheit, den Fahrerkomfort und die Akzeptanz von automatisierten Fahrzeugen. Eine erfolgreiche Interaktion erfordert eine präzise Interpretation aller Einflussfaktoren wie dem Fahrerzustand, dem Systemzustand und den Umwelteinflüssen (z. B. Verkehr, Wetter). In diesem Beitrag wird eine detaillierte Struktur der Fahrer-Fahrzeug-Interaktion diskutiert, welche die Fahrsituation und den Fahrerzustand berücksichtigt, um anschließend die Leistung des Fahrers zu verbessern. Die Interaktion wird von einem Regler geleitet, der den Fahrerzustand als Eingang innerhalb einer Schleife erhält. Die Regelung des Fahrerzustands erfolgt bis der Sollzustand erreicht wird. Darüber hinaus wird ein Fahrermodell vorgeschlagen, das den Entscheidungsprozess des Fahrers während der Interaktion zwischen dem Fahrer und dem Fahrzeug und während des Übergangs der Fahraufgaben darstellt. Das Modell umfasst den sensorischen Wahrnehmungsprozess, die Entscheidungsfindung und die motorische Reaktion. Der Entscheidungsprozess während der Interaktion befasst sich mit den kognitiven und emotionalen Zuständen des Fahrers. Auf der Grundlage der vorgeschlagenen Fahrer-Fahrzeug-Interaktionsschleife und des Fahrermodells wird ein Experiment mit 38 Teilnehmern in einem Fahrsimulator durchgeführt, um zu untersuchen, (1) ob sowohl emotionale als auch kognitive Zustände während des Entscheidungsprozesses aktiv werden und (2) wie die zeitliche Abfolge der Prozesse aussieht. Schließlich werden die aus dem Experiment gewonnenen Daten analysiert. Die Ergebnisse stimmen mit dem vorgeschlagenen Fahrermodell in Bezug auf den kognitiven und emotionalen Zustand des Fahrers während des Moduswechsels vom automatisierten System zum menschlichen Fahrer überein

Does Order Matter? Investigating the Effect of Sequence on Glance Duration During On-Road Driving

Previous literature has shown that vehicle crash risks increases as drivers’ off-road glance duration increases. Many factors influence drivers’ glance duration such as individual differences, driving environment, or task characteristics. Theories and past studies suggest that glance duration increases as the task progresses, but the exact relationship between glance sequence and glance durations is not fully understood. The purpose of this study was to examine the effect of glance sequence on glance duration among drivers completing a visual-manual radio tuning task and an auditory-vocal based multi-modal navigation entry task. Eighty participants drove a vehicle on urban highways while completing radio tuning and navigation entry tasks. Forty participants drove under an experimental protocol that required three button presses followed by rotation of a tuning knob to complete the radio tuning task while the other forty participants completed the task with one less button press. Multiple statistical analyses were conducted to measure the effect of glance sequence on glance duration. Results showed that across both tasks and a variety of statistical tests, glance sequence had inconsistent effects on glance duration—the effects varied according to the number of glances, task type, and data set that was being evaluated. Results suggest that other aspects of the task as well as interface design effect glance duration and should be considered in the context of examining driver attention or lack thereof. All in all, interface design and task characteristics have a more influential impact on glance duration than glance sequence, suggesting that classical design considerations impacting driver attention, such as the size and location of buttons, remain fundamental in designing in-vehicle interfaces

Integrated Vehicle-Based Safety System heavy truck driver vehicle interface (DVI) literature review

This report was prepared by Battelle, Center for Human Performance and Safety, for UMTRI under contract to the U.S. DOT.The Integrated Vehicle-Based Safety Systems (IVBSS) program is a four-year, two phase cooperative research program conducted by an industry team led by the University of Michigan Transportation Research Institute (UMTRI). The program goal is to integrate several collision warning systems into one vehicle in a way that alerts drivers to potential collision threats with an effective driver vehicle interface (DVI), while minimizing the number of excessive warnings presented to the driver. Basic program strategies for meeting this objective include systematically managing and prioritizing all information presented to the driver, minimizing the number of system false alarms, and restricting auditory alarms to higher urgency collision conditions. The report summarizes existing guidelines, data sources, and design principles relevant to the design of the IVBSS heavy-truck DVI; and discusses high-priority research issues relevant to the development and field testing of the IVBSS heavy-truck DVI.National Highway Traffic Safety Administration, Washington DChttp://deepblue.lib.umich.edu/bitstream/2027.42/58357/1/101059.pd

A Preliminary Assessment of Perceived and Objectively Scaled Workload of a Voice-Based Driver Interface

Interaction with a voice-command interface for radio control, destination entry, MP3 song selection, and phone dialing was assessed along with traditional manual radio control and a multi-level audio–verbal calibration task (nback) on-road in 60 drivers. Subjective workload, compensatory behavior, and physiological indices of cognitive workload suggest that there may be both potential benefits and cautions in the implementation of a representative production level interface

An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.Comment: 15 pages and 5 figures, Submitted to the international conference on Contemporary issues in Data Science (CiDaS 2019), Learn more about this project at https://iasbs.ac.ir/~ansari/fara

Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?

This study demonstrates the need for a holistic safety-impact assessment of an advanced driver assistance system (ADAS) and its effect on eye-glance behaviour. It implements a substantial incremental development of the what-if (counterfactual) simulation methodology, applied to rear-end crashes from the SHRP2 naturalistic driving data. This assessment combines (i) the impact of the change in drivers’ off-road glance behaviour due to the presence of the ADAS, and (ii) the safety impact of the ADAS alone. The results illustrate how the safety benefit of forward collision warning and autonomous emergency braking, in combination with adaptive cruise control (ACC) and driver assist (DA) systems, may almost completely dominate the safety impact of the longer off-road glances that activated ACC and DA systems may induce. Further, this effect is shown to be robust to induced system failures. The accuracy of these results is tempered by outlined limitations, which future estimations will benefit from addressing. On the whole, this study is a further step towards a successively more accurate holistic risk assessment which includes driver behavioural responses such as off-road glances together with the safety effects provided by the ADAS

User-centered design of an instruction manual for a research vehicle

Liikennetutkimus on akateemisesti ja teollisesti yhä tärkeämpi aihealue ajoneuvojen ja tieinfrastruktuurin jatkuvasti kehittyessä. Liikenteen ja liikenneympäristön tutkiminen muiden tienkäyttäjien huomaamatta vaatii tarkoituksenmukaisia työkaluja. Aalto-yliopiston instrumentoitu tutkimusajoneuvo on rakennettu vastaamaan juuri tähän tarpeeseen. Ajoneuvo on varustettu useilla erilaisilla antureilla ja kameroilla ympäröivän liikennetilanteen tallentamiseksi ja analysoimiseksi. Perinteisesti instrumentoidun ajoneuvon kaltaisen monimutkaisen työkalun käyttö vaatii erityistietoja ja -taitoja. Tämän projektin tarkoituksena oli kehittää ajoneuvolle ohjekirja käyttäjäkeskeisen suunnittelun menetelmin. Iteratiivisen ja kognitiivisen suunnitteluprosessin avulla tuotettu ohjekirja mahdollistaa instrumentoidun ajoneuvon käytön aloittamisen ilman aiempaa kokemusta ja tietopohjaa.Transportation engineering and research remain as focal points in academia and in the industry. The ability to discreetly examine the changing infrastructure and road environment hinges on specialized research tools. The instrumented vehicle of Aalto University caters to this specific research need with an array of sensors and cameras. Operating complex tools usually requires specific knowledge and expertise. In this project, an instruction manual was developed to allow a larger user base to access the instrumented research vehicle with or without prior expertise. An iterative user-centered design philosophy and cognitive engineering principles were used in creating an intuitive and usable manual

Integrated Vehicle-Based Safety System arbitration of heavy truck driver-vehicle interface (DVI) warnings

This report was prepared by Battelle, Center for Human Performance and Safety, for UMTRI under contract to the U.S. DOT.The Integrated Vehicle-Based Safety Systems (IVBSS) program is a four-year, two phase cooperative research program conducted by an industry team led by the University of Michigan Transportation Research Institute (UMTRI). The program goal is to integrate several collision warning systems into one vehicle in a way that alerts drivers to potential collision threats with an effective driver vehicle interface (DVI), while minimizing the number of excessive warnings presented to the driver. Basic program strategies for meeting this objective include systematically managing and prioritizing all information presented to the driver, minimizing the number of system false alarms, and restricting auditory alarms to higher urgency collision conditions. This report describes the methods and results associated with the integration and arbitration of DVI messages for the IVBSS heavy-truck program. The goals of message integration and arbitration were to 1) support a timely and appropriate response from the driver; 2) avoid contributing to driver errors, distraction, confusion, or information overload; and 3) support the development of an accurate and functional mental model of the IVBSS by the driver.National Highway Traffic Safety Administration, Washington DChttp://deepblue.lib.umich.edu/bitstream/2027.42/58359/1/101061.pd

Foundations of Human-Aware Planning -- A Tale of Three Models

abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201