Selection of compressible signals from telemetry data

Sensors are deployed in all aspects of modern city infrastructure and generate vast amounts of data. Only subsets of this data, however, are relevant to individual organisations. For example, a local council may collect suspension movement from vehicles to detect pot-holes, but this data is not relevant when assessing traffic flow. Supervised feature selection aims to find the set of signals that best predict a target variable. Typical approaches use either measures of correlation or similarity, as in filter methods, or predictive power in a learned model, as in wrapper methods. In both approaches selected features often have high entropies and are not suitable for compression. This is of particular issue in the automotive domain where fast communication and archival of vehicle telemetry data is likely to be prevalent in the near future, especially with technologies such as V2V and V2X. In this paper, we adapt a popular feature selection filter method to consider the compressibility of signals being selected for use in a predictive model. In particular, we add a compression term to the Minimal Redundancy Maximal Relevance (MRMR) filter and introduce Minimal Redundancy Maximal Relevance And Compression (MRMRAC). Using MRMRAC, we then select features from the Controller Area Network (CAN) and predict each of current instantaneous fuel consumption, engine torque, vehicle speed, and gear position, using a Support Vector Machine (SVM). We show that while performance is slightly lower when compression is considered, the compressibility of the selected features is significantly improved

Evaluating secondary input devices to support an automotive touchscreen HMI: a cross-cultural simulator study conducted in the UK and China

Touchscreen Human-Machine Interfaces (HMIs) are a well-established and popular choice to provide the primary control interface between driver and vehicle, yet inherently demand some visual attention. Employing a secondary device with the touchscreen may reduce the demand but there is some debate about which device is most suitable, with current manufacturers favouring different solutions and applying these internationally. We present an empirical driving simulator study, conducted in the UK and China, in which 48 participants undertook typical in-vehicle tasks utilising either a touchscreen, rotary-controller, steering-wheel-controls or touchpad. In both the UK and China, the touchscreen was the most preferred/least demanding to use, and the touchpad least preferred/most demanding, whereas the rotary-controller was generally favoured by UK drivers and steering-wheel-controls were more popular in China. Chinese drivers were more excited by the novelty of the technology, and spent more time attending to the devices while driving, leading to an increase in off-road glance time and a corresponding detriment to vehicle control. Even so, Chinese drivers rated devices as easier-to-use while driving, and felt that they interfered less with their driving performance, compared to their UK counterparts. Results suggest that the most effective solution (to maximise performance/acceptance, while minimising visual demand) is to maintain the touchscreen as the primary control interface (e.g. for top-level tasks), and supplement this with a secondary device that is only enabled for certain actions; moreover, different devices may be employed in different cultural markets. Further work is required to explore these recommendations in greater depth (e.g. during extended or real-world testing), and to validate the findings and approach in other cultural contexts

An investigation of the effects of driver age when using novel navigation systems in a head-up display

Although drivers gain experience with age, many older drivers are faced with age-related deteriorations that can lead to a higher crash risk. Head-Up Displays (HUDs) have been linked to significant improvements in driving performance for older drivers by tackling issues related to aging. For this study, two Augmented Reality (AR) HUD virtual car navigation solutions were tested (one screen-fixed, one world-fixed), aiming to improve navigation performance and reduce the discrepancy between younger and older drivers by aiding the appropriate allocation of attention and easing interpretation of navigational information. Twenty-five participants (12 younger, 13 older) undertook a series of drives within a medium-fidelity simulator with three different navigational conditions (virtual car HUD, static HUD arrow graphic and traditional head-down satnav). Results showed that older drivers tended to achieve navigational success rates similar to the younger group, but experienced higher objective mental workload. Solely for the static HUD arrow graphic, differences in most workload questionnaire items and objective workload between younger and older participants were not significant. The virtual car led to improved navigation performance of all drivers, compared to the other systems. Hence, both AR HUD systems show potential for older drivers, which needs to be further investigated in a real-world driving context

User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle

Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system’s status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers’ expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone

Using the ideas café to explore trust in autonomous vehicles

© Springer International Publishing AG, part of Springer Nature 2019. Trust has been shown to play a key role in our ability to safely use autonomous vehicles; hence the authors used the Ideas Café to explore the factors affecting trust in autonomous vehicles. The Ideas Café is an informal collaborative event that brings the public together with domain experts for exploratory research. The authors structured the event around factors affecting trust in the technology, privacy and societal impact. The event followed a mixed methods approach using: table discussions, spectrum lines and line ups. 36 participants attended the Ideas Café event held at the Coventry Transport Museum in June 2017. Table discussions provided the key findings for Thematic Analysis as part of Grounded Theory; which found, contrary to current research trends, designing for the technology’s integration with society as equally important for trust as the vehicle design itself. The authors also reported on the emergent high level interface guidelines

Enabling Multitasking by Designing for Situation Awareness within the Vehicle Environment

In the driving environment, competition exists between Driving Related Activities (DRAs) and Non-Driving Related Activities (NDRAs). This is a source of inattention and human error. Continual proliferation of in-vehicle information systems (IVIS) presents drivers with opportunities for distraction. Drivers simultaneously manage DRAs alongside unrelated but cognitively demanding NDRAs. Vehicle designers need ways of understanding human capability in such situations to provide solutions that accommodate these conflicting demands. This paper proposes a framework intended to address such challenges, rooted in the widely accepted construct of Situation Awareness (SA). However, SA theory does not presently accommodate disparate unrelated goal-driven tasks performed in parallel. This framework reconciles the present reality of drivers simultaneously devoting cognitive resources to attain SA for multiple activities by proposing a separate body of knowledge for each active goal. Additionally, the process of achieving SA is expanded to incorporate this concurrent development of separate bodies of goal-directed knowledge. The advantage of reconceptualising SA for driving allows consideration of interface design which minimises the impact of competing activities. The aim is a framework facilitating creation of IVIS that help drivers succeed in multi-goal multitasking situations. Implications of the proposed framework for theory, design, and industry-driven automotive safety efforts are discussed.This research is funded by Jaguar Land Rover Research Department in collaboration with the Centre for Advanced Photonics and Electronics (CAPE) and the Engineering Design Centre at Cambridge University

Emergence of norms in interactions with complex rewards

Autonomous agents are becoming increasingly ubiquitous and are playing an increasing role in wide range of safety-critical systems, such as driverless cars, exploration robots and unmanned aerial vehicles. These agents operate in highly dynamic and heterogeneous environments, resulting in complex behaviour and interactions. Therefore, the need arises to model and understand more complex and nuanced agent interactions than have previously been studied. In this paper, we propose a novel agent-based modelling approach to investigating norm emergence, in which such interactions can be investigated. To this end, while there may be an ideal set of optimally compatible actions there are also combinations that have positive rewards and are also compatible. Our approach provides a step towards identifying the conditions under which globally compatible norms are likely to emerge in the context of complex rewards. Our model is illustrated using the motivating example of self-driving cars, and we present the scenario of an autonomous vehicle performing a left-turn at a T-intersectio

Influence of traffic context and information presentation on evaluation of autonomous highway journeys

Previous research into perceptions of autonomous vehicles has largely focused on a priori attitudes, with little work on the perception of specific traffic situations, context and driving styles. The present study used three simulator experiments (total N = 150) to examine the combined effects of vehicle speed, lane position, information presentation and traffic context on occupants’ levels of satisfaction with autonomous highway journeys. Overall, occupants preferred being in a vehicle that was mostly overtaking compared to being overtaken, regardless of whether the overtaking vehicles were exceeding the speed limit. This finding remained even when occupants were given additional reminders that they themselves were travelling at an appropriate speed (Experiments 1 & 2). Experiment 3 found that occupants preferred overtaking to being overtaken when following another car, but this preference disappeared when they were following a lorry, suggesting that occupants’ sensitivity to position amongst the traffic was partially context dependent. Overall, the findings suggest that journey satisfaction is sensitive to overtaking contexts and the inappropriate behaviour of other drivers (e.g., speeding) can reduce journey satisfaction for occupants in autonomous vehicles that drive within the speed limit, depending on the specific traffic situation. Potential implications for the integration of autonomous vehicles with other traffic and the need for in-vehicle presentation of information are discussed

Enabling multitasking by designing for situation awareness within the vehicle environment

In the driving environment, competition exists between Driving Related Activities (DRAs) and Non-Driving Related Activities (NDRAs). This is a source of inattention and human error. Continual proliferation of in-vehicle information systems (IVIS) presents drivers with opportunities for distraction. Drivers simultaneously manage DRAs alongside unrelated but cognitively demanding NDRAs. Vehicle designers need ways of understanding human capability in such situations to provide solutions that accommodate these conflicting demands. This paper proposes a framework intended to address such challenges, rooted in the widely accepted construct of Situation Awareness (SA). However, SA theory does not presently accommodate disparate unrelated goal-driven tasks performed in parallel. This framework reconciles the present reality of drivers simultaneously devoting cognitive resources to attain SA for multiple activities by proposing a separate body of knowledge for each active goal. Additionally, the process of achieving SA is expanded to incorporate this concurrent development of separate bodies of goal-directed knowledge. The advantage of reconceptualising SA for driving allows consideration of interface design which minimises the impact of competing activities. The aim is a framework facilitating creation of IVIS that help drivers succeed in multi-goal multitasking situations. Implications of the proposed framework for theory, design and industry-driven automotive safety efforts are discussed

Data for Cooperative object classification for driving applications

3D object classification can be realised by rendering views of the same object from different angles and aggregating all the views to build a classifier. Although this approach has been previously proposed for general objects classification, most existing works did not consider visual impairments. In contrast, this paper considers the problem of 3D object classification for driving applications under impairments (e.g. occlusion and sensor noise) by generating an application-specific dataset. We present a cooperative object classification method where multiple images of the same object seen from different perspectives (agents) are exploited to generate more accurate classification. We consider model generalisation capability and its resilience to impairments. We introduce an occlusion model with higher resemblance to real-world occlusion and use a simplified sensor noise model. The experimental results show that the cooperative model, relying on multiple views, significantly outperforms single-view methods and is effective in mitigating the effects of occlusion and sensor noise