The psychology of driving automation: A discussion with Professor Don Norman

Introducing automation into automobiles had inevitable consequences for the driver and driving. Systems that automate longitudinal and lateral vehicle control may reduce the workload of the driver. This raises questions of what the driver is able to do with this 'spare' attentional capacity. Research in our laboratory suggests that there is unlikely to be any spare capacity because the attentional resources are not 'fixed'. Rather, the resources are inextricably linked to task demand. This paper presents some of the arguments for considering the psychological aspects of the driver when designing automation into automobiles. The arguments are presented in a conversation format, based on discussions with Professor Don Norman. Extracts from relevant papers to support the arguments are presented

Spatial judgment in Parkinson's disease: Contributions of attentional and executive dysfunction

Spatial judgment is impaired in Parkinson's disease (PD), with previous research suggesting that disruptions in attention and executive function are likely contributors. If judgment of center places demands on frontal systems, performance on tests of attention/executive function may correlate with extent of bias in PD, and attentional disturbance may predict inconsistency in spatial judgment. The relation of spatial judgment to attention/executive function may differ for those with left-side versus right-side motor onset (LPD, RPD), reflecting effects of attentional lateralization. We assessed 42 RPD, 37 LPD, and 67 healthy control participants with a Landmark task (LM) in which a cursor moved horizontally from the right (right-LM) or left (left-LM). The task was to judge the center of the line. Participants also performed neuropsychological tests of attention and executive function. LM group differences were found on left-LM only, with both PD subgroups biased leftward of the control group (RPD p < .05; LPD p < .01; no RPD-LPD difference). For left-LM trials, extent of bias significantly correlated with performance on the cognitive tasks for PD but not for the control group. PD showed greater variability in perceived center than the control group; this variability correlated with performance on the cognitive tasks. The correlations between performance on the test of spatial judgment and the tests of attention/executive function suggest that frontal-based attentional dysfunction affects dynamic spatial judgment, both in extent of spatial bias and in consistency of response as indexed by intertrial variability. (PsycINFO Database Record (c) 2019 APA, all rights reserved).R01 NS067128 - NINDS NIH HHS; R21 NS043730 - NINDS NIH HHS; National Institute of Neurological Disorders and Stroke; American Parkinson's Disease Association; Massachusetts ChapterAccepted manuscrip

Right-lateralised lane keeping in young and older British drivers

Young adults demonstrate a small, but consistent, asymmetry of spatial attention favouring the left side of space (“pseudoneglect”) in laboratory-based tests of perception. Conversely, in more naturalistic environments, behavioural errors towards the right side of space are often observed. In the older population, spatial attention asymmetries are generally diminished, or even reversed to favour the right side of space, but much of this evidence has been gained from lab-based and/or psychophysical testing. In this study we assessed whether spatial biases can be elicited during a simulated driving task, and secondly whether these biases also shift with age, in line with standard lab-based measures. Data from 77 right-handed adults with full UK driving licences (i.e. prior experience of left-lane driving) were analysed: 38 young (mean age = 21.53) and 39 older adults (mean age = 70.38). Each participant undertook 3 tests of visuospatial attention: the landmark task, line bisection task, and a simulated lane-keeping task. We found leftward biases in young adults for the landmark and line bisection tasks, indicative of pseudoneglect, and a mean lane position towards the right of centre. In young adults the leftward landmark task biases were negatively correlated with rightward lane-keeping biases, hinting that a common property of the spatial attention networks may have influenced both tasks. As predicted, older adults showed no group-level spatial asymmetry on the landmark nor the line bisection task, but they maintained a mean rightward lane position, similar to young adults. The 3 tasks were not inter-correlated in the older group. These results suggest that spatial biases in older adults may be elicited more effectively in experiments involving complex behaviour rather than abstract, lab-based measures. More broadly, these results confirm that lateral biases of spatial attention are linked to driving behaviour, and this could prove informative in the development of future vehicle safety and driving technology

SBNet: Sparse Blocks Network for Fast Inference

Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications. For many problems such as object detection and semantic segmentation, we are able to obtain a low-cost computation mask, either from a priori problem knowledge, or from a low-resolution segmentation network. We show that such computation masks can be used to reduce computation in the high-resolution main network. Variants of sparse activation CNNs have previously been explored on small-scale tasks and showed no degradation in terms of object classification accuracy, but often measured gains in terms of theoretical FLOPs without realizing a practical speed-up when compared to highly optimized dense convolution implementations. In this work, we leverage the sparsity structure of computation masks and propose a novel tiling-based sparse convolution algorithm. We verified the effectiveness of our sparse CNN on LiDAR-based 3D object detection, and we report significant wall-clock speed-ups compared to dense convolution without noticeable loss of accuracy.Comment: 10 pages, CVPR 201

Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems

World-wide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error. The main objective of this research is to develop a predictive system for driving maneuvers by analyzing the cognitive behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle is being driven). Advanced Driving Assistance Systems (ADAS) include different driving functions, such as vehicle parking, lane departure warning, blind spot detection, and so on. While much research has been performed on developing automated co-driver systems, little attention has been paid to the fact that the driver plays an important role in driving events. Therefore, it is crucial to monitor events and factors that directly concern the driver. As a goal, we perform a quantitative and qualitative analysis of driver behavior to find its relationship with driver intentionality and driving-related actions. We have designed and developed an instrumented vehicle (RoadLAB) that is able to record several synchronized streams of data, including the surrounding environment of the driver, vehicle functions and driver cephalo-ocular behavior, such as gaze/head information. We subsequently analyze and study the behavior of several drivers to find out if there is a meaningful relation between driver behavior and the next driving maneuver