Analysis of Disengagements in Semi-Autonomous Vehicles: Drivers’ Takeover Performance and Operational Implications

This report analyzes the reactions of human drivers placed in simulated Autonomous Technology disengagement scenarios. The study was executed in a human-in-the-loop setting, within a high-fidelity integrated car simulator capable of handling both manual and autonomous driving. A population of 40 individuals was tested, with metrics for control takeover quantification given by: i) response times (considering inputs of steering, throttle, and braking); ii) vehicle drift from the lane centerline after takeover as well as overall (integral) drift over an S-turn curve compared to a baseline obtained in manual driving; and iii) accuracy metrics to quantify human factors associated with the simulation experiment. Independent variables considered for the study were the age of the driver, the speed at the time of disengagement, and the time at which the disengagement occurred (i.e., how long automation was engaged for). The study shows that changes in the vehicle speed significantly affect all the variables investigated, pointing to the importance of setting up thresholds for maximum operational speed of vehicles driven in autonomous mode when the human driver serves as back-up. The results shows that the establishment of an operational threshold could reduce the maximum drift and lead to better control during takeover, perhaps warranting a lower speed limit than conventional vehicles. With regards to the age variable, neither the response times analysis nor the drift analysis provide support for any claim to limit the age of drivers of semi-autonomous vehicles

ON THE INFLUENCE OF SOCIAL ROBOTS IN COGNITIVE MULTITASKING AND ITS APPLICATION

[Objective] I clarify the impact of social robots on cognitive tasks, such as driving a car or driving an airplane, and show the possibility of industrial applications based on the principles of social robotics. [Approach] I adopted the MATB, a generalized version of the automobile and airplane operation tasks, as cognitive tasks to evaluate participants' performance on reaction speed, tracking performance, and short-term memory tasks that are widely applicable, rather than tasks specific to a particular situation. Also, as the stimuli from social robots, we used the iCub robot, which has been widely used in social communication research. In the analysis of participants, I not only analyzed performance, but also mental workload using skin conductance and emotional analysis of arousal-valence using facial expressions analysis. In the first experiment, I compared a social robot that use social signals with a nonsocial robot that do not use such signals and evaluated whether social robots affect cognitive task performances. In the second experiment, I focused on vitality forms and compared a calm social robot with an assertive social robot. As analysis methods, I adopted Mann-Whitney's U test for one-pair comparisons, and ART-ANOVA for analysis of variance in repeated task comparisons. Based on the results, I aimed to express vitality forms in a robot head, which is smaller in size and more flexible in placement than a full-body humanoid robot, considering car and airplane cockpit's limited space. For that, I developed a novel eyebrow and I decided to use a wire-driven technique, which is widely used in surgical robots to control soft materials. [Main results] In cognitive tasks such as car drivers and airplane pilots, I clarified the effects of social robots acting social behaviors on task performance, mental workload, and emotions. In addition, I focused on vitality forms, one of the parameters of social behaviors, and clarified the effects of different vitality forms of social robots' behavior on cognitive tasks.In cognitive tasks such as car drivers and airplane pilots, we clarified the effects of social robots acting in social behaviors on task performance, mental workload, and emotions, and showed that the presence of social robots can be effective in cognitive tasks. Furthermore, focusing on vitality forms, one of the parameters of social behaviors, we clarified the effects of different vitality forms of social robots' behaviors on cognitive tasks, and found that social robots with calm behaviors positively affected participants' facial expressions and improved their performance in a short-term memory task. Based on the results, I decided to adopt the configuration of a robot head, eliminating the torso from the social humanoid robot, iCub, considering the possibility of placement in a limited space such as cockpits of car or airplane. In designing the robot head, I developed a novel soft-material eyebrow that can be mounted on the iCub robot head to achieve continuous position and velocity changes, which is an important factor to express vitality forms. The novel eyebrows can express different vitality forms by changing the shape and velocity of the eyebrows, which was conventionally represented by the iCub's torso and arms. [Significance] The results of my research are important achievements that opens up the possibility of applying social robots to non-robotic industries such as automotive and aircraft. In addition, the newly developed soft-material eyebrows' precise shape and velocity changes have opened up new research possibilities in social robotics and social communication research themselves, enabling experiments with complex facial expressions that move beyond Ekman's simple facial expression changes definition, such as, joy, anger, sadness, and pleasure. Thus, the results of this research are one important step in both scientific and industrial applications. [Key-words] social robot, cognitive task, vitality form, robot head, facial expression, eyebro

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Computational driver behavior models for vehicle safety applications

The aim of this thesis is to investigate how human driving behaviors can be formally described in mathematical models intended for online personalization of advanced driver assistance systems (ADAS) or offline virtual safety evaluations. Both longitudinal (braking) and lateral (steering) behaviors in routine driving and emergencies are addressed. Special attention is paid to driver glance behavior in critical situations and the role of peripheral vision.First, a hybrid framework based on autoregressive models with exogenous input (ARX-models) is employed to predict and classify driver control in real time. Two models are suggested, one targeting steering behavior and the other longitudinal control behavior. Although the predictive performance is unsatisfactory, both models can distinguish between different driving styles.Moreover, a basic model for drivers\u27 brake initiation and modulation in critical longitudinal situations (specifically for rear-end conflicts) is constructed. The model is based on a conceptual framework of noisy evidence accumulation and predictive processing. Several model extensions related to gaze behavior are also proposed and successfully fitted to real-world crashes and near-crashes. The influence of gaze direction is further explored in a driving simulator study, showing glance response times to be independent of the glance\u27s visual eccentricity, while brake response times increase for larger gaze angles, as does the rate of missed target detections.Finally, the potential of a set of metrics to quantify subjectively perceived risk in lane departure situations to explain drivers\u27 recovery steering maneuvers was investigated. The most influential factors were the relative yaw angle and splay angle error at steering initiation. Surprisingly, it was observed that drivers often initiated the recovery steering maneuver while looking off-road.To sum up, the proposed models in this thesis facilitate the development of personalized ADASs and contribute to trustworthy virtual evaluations of current, future, and conceptual safety systems. The insights and ideas contribute to an enhanced, human-centric system development, verification, and validation process. In the long term, this will likely lead to improved vehicle safety and a reduced number of severe injuries and fatalities in traffic

Situational Awareness, Driver’s Trust in Automated Driving Systems and Secondary Task Performance

Driver assistance systems, also called automated driving systems, allow drivers to immerse themselves in non-driving-related tasks. Unfortunately, drivers may not trust the automated driving system, which prevents either handing over the driving task or fully focusing on the secondary task. We assert that enhancing situational awareness can increase a driver's trust in automation. Situational awareness should increase a driver's trust and lead to better secondary task performance. This study manipulated driversʼ situational awareness by providing them with different types of information: the control condition provided no information to the driver, the low condition provided a status update, while the high condition provided a status update and a suggested course of action. Data collected included measures of trust, trusting behavior, and task performance through surveys, eye-tracking, and heart rate data. Results show that situational awareness both promoted and moderated the impact of trust in the automated vehicle, leading to better secondary task performance. This result was evident in measures of self-reported trust and trusting behavior.This research was supported in part by the Automotive Research Center (ARC) at the University of Michigan, with funding from government contract Department of the Army W56HZV-14-2-0001 through the U. S. Army Tank Automotive Research, Development, and Engineering Center (TARDEC). The authors acknowledge and greatly appreciate the guidance of Victor Paul (TARDEC), Ben Haynes (TARDEC), and Jason Metcalfe (ARL) in helping design the study. The authors would also like to thank Quantum Signal, LLC, for providing its ANVEL software and invaluable development support.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148141/1/SA Trust - SAE- Public.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148141/4/Petersen et al. 2019.pdfDescription of Petersen et al. 2019.pdf : Final Publication Versio

Real-Time Estimation of Drivers' Trust in Automated Driving Systems

Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of techniques for measuring drivers' trust in the automated driving system during real-time applications execution. One possible approach for measuring trust is through modeling its dynamics and subsequently applying classical state estimation methods. This paper proposes a framework for modeling the dynamics of drivers' trust in automated driving systems and also for estimating these varying trust levels. The estimation method integrates sensed behaviors (from the driver) through a Kalman lter-based approach. The sensed behaviors include eye-tracking signals, the usage time of the system, and drivers' performance on a non-driving-related task (NDRT). We conducted a study (n = 80) with a simulated SAE level 3 automated driving system, and analyzed the factors that impacted drivers' trust in the system. Data from the user study were also used for the identi cation of the trust model parameters. Results show that the proposed approach was successful in computing trust estimates over successive interactions between the driver and the automated driving system. These results encourage the use of strategies for modeling and estimating trust in automated driving systems. Such trust measurement technique paves a path for the design of trust-aware automated driving systems capable of changing their behaviors to control drivers' trust levels to mitigate both undertrust and overtrust.National Science FoundationBrazilian Army's Department of Science and TechnologyAutomotive Research Center (ARC) at the University of MichiganU.S. Army CCDC/GVSC (government contract DoD-DoA W56HZV14-2-0001).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162572/1/Azevedo Sa et al. 2020.pdfSEL

Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, a generalizable, interpretable, computational model of adaptive human driving behavior is still lacking. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.Comment: 33 pages, 13 figure