Survey and Synthesis of State of the Art in Driver Monitoring

Consumer demand for partial driving automation and hands-free driving capability

INTRODUCTION

It is often assumed that consumers want partial driving automation in their vehicles, yet there has been little research on the topic. Also unclear is what the public's appetite is for hands-free driving capability, automated (auto)-lane-change functionality, and driver monitoring that helps reinforce proper use of these features.

METHOD

Through an internet-based survey of a nationally representative sample of 1,010 U.S. adult drivers, this study explored consumer demand for different aspects of partial driving automation.

RESULTS

Eighty percent of drivers want to use lane centering, but more prefer versions with a hands-on-wheel requirement (36%) than hands-free (27%). More than half of drivers are comfortable with different driver monitoring strategies, but comfort level is related to perceptions of feeling safer with it given its role in helping drivers use the technology properly. People who prefer hands-free lane centering are the most accepting of other vehicle technologies, including driver monitoring, but some also indicate an intent to misuse these features. The public is somewhat more reluctant to accept auto lane change, with 73% saying they would use it, and more often prefer it to be driver-initiated (45%) than vehicle-initiated (14%). More than three quarters of drivers want auto lane change to have a hands-on-wheel requirement.

CONCLUSION

Consumers are interested in partial driving automation, but there is resistance to more sophisticated functionality, especially vehicle-initiated auto lane change, in a vehicle that cannot technically drive itself.

PRACTICAL APPLICATIONS

This study confirms the public's appetite for partial driving automation and possible intention for misuse. It is imperative that the technology be designed in ways that deter such misuse. The data suggest that consumer information, including marketing, has a role to play to communicate the purpose and safety value of driver monitoring and other user-centric design safeguards to promote their implementation, acceptance, and safe adoption.

Artificial Proprioceptive Reflex Warning Using EMG in Advanced Driving Assistance System

A frequent cause of auto accidents is disregarding the proximal traffic of an ego-vehicle during lane changing. Presumably, in a split-second-decision situation we may prevent an accident by predicting the intention of a driver before her action onset using the neural signals data, meanwhile building the perception of surroundings of a vehicle using optical sensors. The prediction of an intended action fused with the perception can generate an instantaneous signal that may replenish the driver's ignorance about the surroundings. This study examines electromyography (EMG) signals to predict intention of a driver along perception building stack of an autonomous driving system (ADS) in building an advanced driving assistant system (ADAS). EMG are classified into left-turn and right-turn intended actions and lanes and object detection with camera and Lidar are used to detect vehicles approaching from behind. A warning issued before the action onset, can alert a driver and may save her from a fatal accident. The use of neural signals for intended action prediction is a novel addition to camera, radar and Lidar based ADAS systems. Furthermore, the study demonstrates efficacy of the proposed idea with experiments designed to classify online and offline EMG data in real-world settings with computation time and the latency of communicated warnings.

Robotic Vision for Human-Robot Interaction and Collaboration: A Survey and Systematic Review

Robotic vision for human-robot interaction and collaboration is a critical process for robots to collect and interpret detailed information related to human actions, goals, and preferences, enabling robots to provide more useful services to people. This survey and systematic review presents a comprehensive analysis on robotic vision in human-robot interaction and collaboration over the last 10 years. From a detailed search of 3850 articles, systematic extraction and evaluation was used to identify and explore 310 papers in depth. These papers described robots with some level of autonomy using robotic vision for locomotion, manipulation and/or visual communication to collaborate or interact with people. This paper provides an in-depth analysis of current trends, common domains, methods and procedures, technical processes, data sets and models, experimental testing, sample populations, performance metrics and future challenges. This manuscript found that robotic vision was often used in action and gesture recognition, robot movement in human spaces, object handover and collaborative actions, social communication and learning from demonstration. Few high-impact and novel techniques from the computer vision field had been translated into human-robot interaction and collaboration. Overall, notable advancements have been made on how to develop and deploy robots to assist people.

Challenges of Large-Scale Multi-Camera Datasets for Driver Monitoring Systems

Tremendous advances in advanced driver assistance systems (ADAS) have been possible thanks to the emergence of deep neural networks (DNN) and Big Data (BD) technologies. Huge volumes of data can be managed and consumed as training material to create DNN models which feed functions such as lane keeping systems (LKS), automated emergency braking (AEB), lane change assistance (LCA), etc. In the ADAS/AD domain, these advances are only possible thanks to the creation and publication of large and complex datasets, which can be used by the scientific community to benchmark and leverage research and development activities. In particular, multi-modal datasets have the potential to feed DNN that fuse information from different sensors or input modalities, producing optimised models that exploit modality redundancy, correlation, complementariness and association. Creating such datasets pose a scientific and engineering challenge. The BD dimensions to cover are volume (large datasets), variety (wide range of scenarios and context), veracity (data labels are verified), visualization (data can be interpreted) and value (data is useful). In this paper, we explore the requirements and technical approach to build a multi-sensor, multi-modal dataset for video-based applications in the ADAS/AD domain. The Driver Monitoring Dataset (DMD) was created and partially released to foster research and development on driver monitoring systems (DMS), as it is a particular sub-case which receives less attention than exterior perception. Details on the preparation, construction, post-processing, labelling and publication of the dataset are presented in this paper, along with the announcement of a subsequent release of DMD material publicly available for the community.

Eye Tracking in Driver Attention Research-How Gaze Data Interpretations Influence What We Learn

Eye tracking (ET) has been used extensively in driver attention research. Amongst other findings, ET data have increased our knowledge about what drivers look at in different traffic environments and how they distribute their glances when interacting with non-driving related tasks. Eye tracking is also the go-to method when determining driver distraction via glance target classification. At the same time, eye trackers are limited in the sense that they can only objectively measure the gaze direction. To learn more about why drivers look where they do, what information they acquire foveally and peripherally, how the road environment and traffic situation affect their behavior, and how their own expertise influences their actions, it is necessary to go beyond counting the targets that the driver foveates. In this perspective paper, we suggest a glance analysis approach that classifies glances based on their purpose. The main idea is to consider not only the intention behind each glance, but to also account for what is relevant in the surrounding scene, regardless of whether the driver has looked there or not. In essence, the old approaches, unaware as they are of the larger context or motivation behind eye movements, have taken us as far as they can. We propose this more integrative approach to gain a better understanding of the complexity of drivers' informational needs and how they satisfy them in the moment.