The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarios

In the development of highly automated driving systems (L3 and 4), much research has been done on the subject of driver takeover. Strong focus has been placed on the takeover quality. Previous research has shown that one of the main influencing factors is the complexity of a traffic situation that has not been sufficiently addressed so far, as different approaches towards complexity exist. This paper differentiates between the objective complexity and the subjectively perceived complexity. In addition, the familiarity with a takeover situation is examined. Gold et al. show that repetition of takeover scenarios strongly influences the take-over performance. Yet, both complexity and familiarity have not been considered at the same time. Therefore, the aim of the present study is to examine the impact of objective complexity and familiarity on the subjectively perceived complexity and the resulting takeover quality. In a driving simulator study, participants are requested to take over vehicle control in an uncritical situation. Familiarity and objective complexity are varied by the number of surrounding vehicles and scenario repetitions. Subjective complexity is measured using the NASA-TLX; the takeover quality is gathered using the take-over controllability rating (TOC-Rating). The statistical evaluation results show that the parameters significantly influence the takeover quality. This is an important finding for the design of cognitive assistance systems for future highly automated and intelligent vehicles

Potential benefits of an adaptive forward collision warning system

Forward collision warning (FCW) systems can reduce rear-end vehicle collisions. However, if the presentation of warnings is perceived as mistimed, trust in the system is diminished and drivers become less likely to respond appropriately. In this driving simulator investigation, 45 drivers experienced two FCW systems: a non-adaptive and an adaptive FCW that adjusted the timing of its alarms according to each individual driver’s reaction time. Whilst all drivers benefited in terms of improved safety from both FCW systems, non-aggressive drivers (low sensation seeking, long followers) did not display a preference to the adaptive FCW over its non-adaptive equivalent. Furthermore, there was little evidence to suggest that the non-aggressive drivers’ performance differed with either system. Benefits of the adaptive system were demonstrated for aggressive drivers (high sensation seeking, short followers). Even though both systems reduced their likelihood of a crash to a similar extent, the aggressive drivers rated each FCW more poorly than their non-aggressive contemporaries. However, this group, with their greater risk of involvement in rear-end collisions, reported a preference for the adaptive system as they found it less irritating and stress-inducing. Achieving greater acceptance and hence likely use of a real system is fundamental to good quality FCW design

Smart driving assistance systems : designing and evaluating ecological and conventional displays

In-vehicle information systems have been shown to increase driver workload and cause distraction; both are causal factors for accidents. This simulator study evaluates the impact that two designs for a smart driving aid and scenario complexity has on workload, distraction and driving performance. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely affect driver distraction, while having the effect of decreasing mean driving speed in both the simple and complex driving scenarios. Important differences were also highlighted between conventional and ecologically designed smart driving interfaces with respect to subjective workload and peripheral detection

An Open-Source Microscopic Traffic Simulator

We present the interactive Java-based open-source traffic simulator available at www.traffic-simulation.de. In contrast to most closed-source commercial simulators, the focus is on investigating fundamental issues of traffic dynamics rather than simulating specific road networks. This includes testing theories for the spatiotemporal evolution of traffic jams, comparing and testing different microscopic traffic models, modeling the effects of driving styles and traffic rules on the efficiency and stability of traffic flow, and investigating novel ITS technologies such as adaptive cruise control, inter-vehicle and vehicle-infrastructure communication

Smart driving aids and their effects on driving performance and driver distraction

In-vehicle information systems have been shown to increase driver workload and cause distraction; both of which are causal factors for accidents. This simulator study evaluates the impact that two designs for a smart driving aid, and scenario complexity have on workload, distraction and driving performance. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely effect driver distraction, while having the effect of decreasing mean driving speed in both the simple and complex driving scenarios. Subjective workload was shown to increase with task difficulty, as well as revealing important differences between the two interface designs

Explainable and Advisable Learning for Self-driving Vehicles

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly

Influence of Task Combination on EEG Spectrum Modulation for Driver Workload Estimation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Objective: This study investigates the feasibility of using a method based on electroencephalography (EEG) for deriving a driver’s mental workload index. Background: The psychophysiological signals provide sensitive information for human functional states assessment in both laboratory and real-world settings and for building a new communication channel between driver and vehicle that allows for driver workload monitoring. Methods: An experiment combining a lane-change task and n-back task was conducted. The task load levels were manipulated in two dimensions, driving task load and working memory load, with each containing three task load conditions. Results: The frontal theta activity showed significant increases in the working memory load dimension, but differences were not found with the driving task load dimension. However, significant decreases in parietal alpha activity were found when the task load was increased in both dimensions. Task-related differences were also found. The driving task load contributed more to the changes in alpha power, whereas the working memory load contributed more to the changes in theta power. Additionally, these two task load dimensions caused significant interactive effects on both theta and alpha power. Conclusion: These results indicate that EEG technology can provide sensitive information for driver workload detection even if the sensitivities of different EEG parameters tend to be task dependent. Application: One potential future application of this study is to establish a general driver workload estimator that uses EEG signals

Sustainability, transport and design: reviewing the prospects for safely encouraging eco-driving

Private vehicle use contributes a disproportionately large amount to the degradation of the environment we inhabit. Technological advancement is of course critical to the mitigation of climate change, however alone it will not suffice; we must also see behavioural change. This paper will argue for the application of Ergonomics to the design of private vehicles, particularly low-carbon vehicles (e.g. hybrid and electric), to encourage this behavioural change. A brief review of literature is offered concerning the effect of the design of a technological object on behaviour, the inter-related nature of goals and feedback in guiding performance, the effect on fuel economy of different driving styles, and the various challenges brought by hybrid and electric vehicles, including range anxiety, workload and distraction, complexity, and novelty. This is followed by a discussion on the potential applicability of a particular design framework, namely Ecological Interface Design, to the design of in-vehicle interfaces that encourage energy-conserving driving behaviours whilst minimising distraction and workload, thus ensuring safety