Ecological IVIS design : using EID to develop a novel in-vehicle information system

New in-vehicle information systems (IVIS) are emerging which purport to encourage more environment friendly or ‘green’ driving. Meanwhile, wider concerns about road safety and in-car distractions remain. The ‘Foot-LITE’ project is an effort to balance these issues, aimed at achieving safer and greener driving through real-time driving information, presented via an in-vehicle interface which facilitates the desired behaviours while avoiding negative consequences. One way of achieving this is to use ecological interface design (EID) techniques. This article presents part of the formative human-centred design process for developing the in-car display through a series of rapid prototyping studies comparing EID against conventional interface design principles. We focus primarily on the visual display, although some development of an ecological auditory display is also presented. The results of feedback from potential users as well as subject matter experts are discussed with respect to implications for future interface design in this field

Ecological interface design for eco-driving

Eco-driving issues are of high priority at the moment. Research suggests that a change in driving style can reduce fuel consumption and emissions by around 15% in many cases. In response to this need, the UK Foot-LITE project developed an in-car feedback system to encourage safer and greener driving behaviours. In order to balance positive behaviour change against the potential negative effects of distraction, an Ecological Interface Design approach was adopted. The current paper presents an overview of the humancentred design process adopted in the Foot-LITE project, as well as a review of other similar systems on the market

Simulation of Mixed Critical In-vehicular Networks

Future automotive applications ranging from advanced driver assistance to autonomous driving will largely increase demands on in-vehicular networks. Data flows of high bandwidth or low latency requirements, but in particular many additional communication relations will introduce a new level of complexity to the in-car communication system. It is expected that future communication backbones which interconnect sensors and actuators with ECU in cars will be built on Ethernet technologies. However, signalling from different application domains demands for network services of tailored attributes, including real-time transmission protocols as defined in the TSN Ethernet extensions. These QoS constraints will increase network complexity even further. Event-based simulation is a key technology to master the challenges of an in-car network design. This chapter introduces the domain-specific aspects and simulation models for in-vehicular networks and presents an overview of the car-centric network design process. Starting from a domain specific description language, we cover the corresponding simulation models with their workflows and apply our approach to a related case study for an in-car network of a premium car

Filter for Car Tracking Based on Acceleration and Steering Angle

The motion of a car is described using a stochastic model in which the driving processes are the steering angle and the tangential acceleration. The model incorporates exactly the kinematic constraint that the wheels do not slip sideways. Two filters based on this model have been implemented, namely the standard EKF, and a new filter (the CUF) in which the expectation and the covariance of the system state are propagated accurately. Experiments show that i) the CUF is better than the EKF at predicting future positions of the car; and ii) the filter outputs can be used to control the measurement process, leading to improved ability to recover from errors in predictive tracking

Judgment and Choice in Personnel Selection

[Excerpt] Imagine that you have set out to buy a used car. You examine eight cars before making your choice, test driving some of them and rejecting others at first glance (due for example to excessive rust). A researcher asks you to rate each of the eight cars in terms of overall quality. The researcher proceeds to sharply criticize you for carrying out an unsystematic search process. Your failure to test-drive every car and to ask the same questions to the dealers about each car has caused you to do a poor job of rank-ordering the cars. You respond that, since you could only afford one car, you had no interest in rank-ordering or in assigning ratings to the entire set of cars. It seems unfair to be criticized for poor performance of a task which was unrelated to your original mission of buying the best used car available. This paper explores the possibility that a similar misspecification of the goals of employee selection has caused researchers to criticize selectors for behavior which may not adversely affect the goal of hiring the best individual from among a group of candidates

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018