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Advanced driver assistance system (ADAS) is the term used to refer to those systems which help the driver either by taking over a task the driver would otherwise have to carry out manually, or by fulfilling a function which is beyond the capabilities of the driver. These systems are predominantly aiming to increase safety.

One of the most commonly used advanced driver assistance system is adaptive cruise control. Although many cars are equipped with such a driver assistance system, the development is still based on subjective evaluation indices. The design of adaptive cruise control could be more powerful and reach more customer acceptance when a design process with specific values to ensure good driving characteristics is used. Such an objective based process is still state of the art of classic chassis development, but typically not used for advanced driver assistance systems.

This paper proposes a method for detecting direction indicators marked on road surfaces for safe driving support. In the proposed method, images are received from a vehicle’s black box, and a method for template matching is used on such direction indicators to detect the indicator area. By detecting the Maximally Stable Extremal Regions (MSER), the matching method is used to detect the road indicator area after the areas where road indicator candidate regions and binary code result images overlap are detected through the multi-level threshold template. The results of the experiment conducted in an actual vehicle driving environment show that, from the total of 270 frames that include indicators, each frame requires approximately 0.34 s, and a minimum of 83 % detection rate is provided.

Safe driving is mandatory for an Advanced Driver Assistance System (ADAS). The adequacy and safety of an ADAS has to be evaluated not only by developers, but also by drivers. Evaluations by drivers can be monitored by their positive and negative emotions, since for example, if they find a hazardous situation, they have a negative emotion, “surprised” or “close call.” An ADAS is considered “good”, if it does not cause driver emotions such as these, and hence, is viewed as adequate and safety. In order to analyze the safety requirements of drivers for the ADAS, we propose a multi-layered scenario analysis (MuLSA). MuLSA is a kind of scenario analysis method consisting of a driver’s journey, his/her emotions, the mechanism of services, as well as the context of the services. This paper shows the results of the observation of emotions through a simulation, and we discuss the effectiveness of analyzing the requirements of services with regard to the user’s emotion.

Within the scope of the European policy for Intelligent Transport Systems (ITS), the PLATA-PROTON project proposes a multi technology cooperative Advanced Driver Assistance System (ADAS), based on the integration of Software-Defined Radio (SDR) devices in vehicles. With the choice of significant road-safety related scenarios, V2V and V2I communications have been both implemented and simulated. This paper proposes an overview of the Software-Defined Radio (SDR) platform development and the performance evaluation based on network simulation, performed within this project.

This paper outlines the use of two complementary software packages that together provide the ability to cover all critical aspects of advanced driver assistance (ADA) system design. One of the software packages (PreScan) focuses on the design of the sensing and control systems, as well as the evaluation for a wide range of traffic and weather scenarios. The other software package (ASM) takes care of the detailed vehicle dynamics and vehicle control. By means of a real-world application example of a Lane Keeping Assistance system, the combined use of these software packages to design and evaluate ADA systems is demonstrated.

The FlexRay protocol was introduced by an international consortium including several car manufacturers to cope with growing real-time requirements of advanced driver assistance functions and safety functions in the automotive domain. The FlexRay protocol offers a static and dynamic segment with a high data rate of 10 Mbit/s. While the event-triggered dynamic segment is used mainly for diagnosis, maintenance, and calibration data, the time-triggered static segment might be used for critical data with strict real-time requirements. In addition to standard linear bus and star topologies, the FlexRay bus allows hybrid topologies including a dual channel mode to increase the reliability. However, in contrast to the prevailing CAN bus [4] in the automotive domain, the configuration of the FlexRay bus is significantly more complex: It requires a large set of parameters and a predefined schedule. This chapter introduces a scheduling concept for the static segment of the FlexRay based on the transformation to a two-dimensional bin packing problem.

This paper advocates that the success and user acceptability of Fully Electric Vehicles (FEVs) will predominantly depend on their electrical energy consumption rate and the corresponding degree of autonomy that they can offer. FEVs must provide their drivers with the highest possible autonomy as well as with a high degree of reliability and robustness in terms of energy performance. The FEV driver must know at all times, with a high degree of assurance, if a destination is reachable with the remaining battery energy, how much energy will be left after his journey, how to efficiently reduce the energy required to reach his destination, as well as when and where it is better to recharge his vehicle. This paper argues that appropriate innovative ICT solutions must be pursued and adopted, in order to assist the driver in dealing with such energy-related issues, and to strengthen FEVs’ autonomy and reliability. Such an ICT solution tailored for FEVs –entitled EcoGem– is described in this paper.

Advanced Driver Assistant Systems (ADAS) are assumed to support drivers in critical traffic situations. This is especially important for older drivers and also drivers with disabilities, whose physical and cognitive resources are limited. An electronic intersection assistant was developed and implemented in a driving simulation setting. Independent variables were users' age and output modality. The utility of visual and auditory interfaces was examined and com pared to a control group which was not assisted. Dependent variables were speed control, accuracy of lane tracking and users' acceptance. Older adults drove significantly slower, but equally accurate than younger drivers. When no assistance was present, driving performance was superior than in both assis tance conditions. The visual interface had a lower detrimental effect than the auditory ADAS which had the strongest distracting effect. In contrast to per formance outcomes, the auditory interface was rated as more helpful by older drivers compared to the visual interface.

Biologically motivated attention systems prefilter the visual environment for scene elements that pop out most or match the current system task best. However, the robustness of biological attention systems is difficult to achieve, given e.g., the high variability of scene content, changes in illumination, and scene dynamics. Most computational attention models do not show real time capability or are tested in a controlled indoor environment only. No approach is so far used in the highly dynamic real world scenario car domain. Dealing with such scenarios requires a strong system adaptation capability with respect to changes in the environment. Here, we focus on five conceptual issues crucial for closing the gap between artificial and natural attention systems operating in the real world. We show the feasibility of our approach on vision data from the car domain. The described attention system is part of a biologically motivated advanced driver assistance system running in real time.

The CARSENSE programme, grouping together 12 major industrial and research partners and sponsored by the EC, is to develop a sensor system, the purpose of which is to provide sufficient information on the car environment at low speeds to assist in low speed driving in complex (urban) environments. This article describes the main objectives of the programme, these being the improvement of the individual sensors and the merger of the information from these sensors in a fusion unit.