Abstract

This document does not aim to review automated vehicles' developments and technologies but automated functions in existing vehicles. Therefore this document reports the results from the benchmarking search carried out within A1.1 for systems that are relevant to the project's preliminary Use Cases of ADAS&ME project. This report reflects the work carried out in order to reach MS2 (M3) of ADAS&ME and the literature review carried out for each affective state. However, it acted as a living document until the submission of D1.1 (M6) to be incorporated in the main body of D1.1. In general, benchmarking is considered a continuous and systematic approach to measure the processes and outputs against the most relevant (and occasionally) toughest competitors in the business, or at least the most prominent/ profitable ones. This is a so called competitive benchmarking approach. Three different types of results are included in this document: a) benchmarking of driver and rider monitoring systems, b) literature search and reviews of literature on drive/rider affective states as defined within DoA and UCs, and, lastly, c) alignment of the SoA collected, systems benchmarked; with key selected organization's priorities and roadmaps about automated vehicle systems and functions. The deliverable opens with the introduction to automation, the objectives of the document, the working group that was involved in A1.1 and worked towards this compilation and the contribution of this document to the selection and refinement of the ADAS&ME Use Case (Chapter 1). An overview of addressed affective states per Use Cases, as they are currently agreed upon, are presented in Chapter 2, along with their general definitions and driver monitoring techniques and systems. Chapter 3 presents an introduction to main research findings per affective state, followed by a literature review overview table, including connection to the project and respective Use Cases. The complete compilation per affective state is included in sub-sections of Annex 1. The table at the end of the Executive Summary includes the number of journals reviewed. In addition, a short literature review was carried out to investigate the automation strategies for stabilisation mechanisms for motorcycles. The work and relevant findings are included in main the document and the rest are also in an Annex. The literatures review is followed by the benchmarking of existing technologies (SoA) for drivers and riders per addressed affective state (Chapter 4). Similarly, an overview of findings is presented in the table at the end of the summary. In addition, studies from other transport modes were reviewed, for their potential of transferring knowledge, methods and techniques. The results are summarised in a Table in Section 4.3, but the whole compilation of the studies was annexed. Chapter 5 presents the alignment of A1.1 results with the efforts of several established organizations, such as ACEM, ERTRAC, NCAP and the international collaborations within CARTRE project. The Deliverable concludes with Chapter 6, where an overview of the results, in terms of numbers and relevance (key categorization) to the project, are presented.

Abstract

Electric vehicles are expected to become one of the key elements of future sustainable transportation systems. The first generation of electric cars are already commercially available but still, suffer from problems and constraints that have to be solved before a mass market might be created. Key aspects that will play an important role in modern electric vehicles are range extension, energy efficiency, safety, comfort as well as communication. An overall solution approach to integrating all these aspects is the development of advanced driver assistance systems to make electric vehicles more intelligent. Driver assistance systems are based on the integration of suitable sensors and actuators as well as electronic devices and software-enabled control functionality to automatically support the human driver. Driver assistance for electric vehicles will differ from the already used systems in fuel-powered cars such as electronic stability programs, adaptive cruise control etc. in a way that they must support energy efficiency while the system itself must also have a low power consumption. In this work, an eco-driving functionality as the first step towards those new driver assistance systems for electric vehicles will be investigated. Using information about the internal state of the car, navigation information as well as advanced information about the environment coming from sensors and network connections, an algorithm will be developed that will adapt the speed of the vehicle automatically to minimize energy consumption. From an algorithmic point of view, a stochastic model predictive control approach will be applied and adapted to the special constraints of the problem. Finally, the solution will be tested in simulations as well as in first experiments with a commercial electric vehicle in the SnT Automation & Robotics Research Group (SnT ARG).

Abstract

AICC (Autonomous Intelligent Cruise Control) systems, as investigated in the European PROMETHEUS and DRIVE projects, should support the driver in longitudinal vehicle control. The presented study analyzed driver- and situation-specific effects, which have to be considered when developing or evaluating AICCs. The investigations focused on assistance systems, which inform or warn a driver in case of inadequate speed or distance. Messages to the driver are transferred either acoustically or visually or via active control devices, such as an “active gas-pedal”. Driving experiments were performed, whereby subjects' distance keeping behavior was monitored. As a first step before experimental evaluation, relevant traffic situations for AICC application were identified and classified. The results of the subsequent situation analysis reveal driver- and situation-specific characteristics. Typical effects are found in frequency and duration of various AICC situations, in distance distribution when following, and in minimum distance when approaching a preceding vehicle. From situation analysis, first consequences were derived showing off capabilities and limitations of AICC systems on principle and of an “active gas-pedal” in particular.