Catálogo de publicaciones - libros

Automotive applications are still driving new MEMS development for new applications. Pressure monitoring application is one of the very dynamic one in particular. This article highlights the evolution of the pressure monitoring sensors both at the component and module level. New product announcements have recently fragmented the product offer in two kinds: Hybrid modules vs. single die packaged modules. This change will strongly affect current player position. This paper will present market needs, status, and trends, to discuss which players will win the race.

Starting with an historical reminder of TPM application, current OEM needs will be described followed by 2006 - 2010 market estimation. In particular, we will focus the discussion on the evolution of the s functions: which level of integration will s support. The final part of the presentation will highlight how the new TPM product offering will impact the industrial chain.

What are the opportunities for MEMS sensors components and what are the new market drivers. What are the opportunities for and current challenges facing MEMS suppliers, and what does this mean for the proportion of income invested in R&D? How does this affect the industry chain and how can suppliers stay on top? To answer these questions we look at market trends in the automotive sector and examine the changing level of involvement of systems and component manufacturers in MEMS along the automotive supply chain.

Increased traffic safety depends on the differentiation in warning, steering actuation, braking interventions as well as the possible passive safety measures in critical situations. Continental built up a test vehicle to develop active safety measures based on radar and camera information. The system focuses on rear end collisions and uses next generation automotive CMOS camera and radar technology to avoid collisions or to reduce their impact severity. The paper describes the networking and the benefit of the additional information, generated by sensor fusion in emergency situations. Key aspects are changes in brake preparation and crash adaptation and the influence of the driver’s behaviour, compared to conventional beam sensor based safety systems.

Tecnalia Automoción is working on new concepts for car cockpit components based on “Ambient Intelligence” (AmI) principles. Related to this concept, Tecnalia Automoción has developed a prototype of a sensorized active headrest to be placed maintaining desired horizontal and vertical safety distances to head and without pyrotechnical actuators which can cause injuries to the user. With the implementation of AmI based solutions like described above, the number of car sensors and actuators will grow up drastically, and more efficient control architectures will be needed. For this, Tecnalia Automoción is designing a networked solution with smart sensors and actuators integrating the IEEE 1451 standard group, initially to be applied to active headrest prototype. IEEE 1451 is a group of seven standards, some of them in revision phase, about smart transducers interface for sensors and actuators that they will let us to have more features in sensor-actuator side.

Measuring the road condition in front of a vehicle could prevent accidents and make technologies like electronic stability control (ESP) more efficient. By making three investigations of the classifications of the four road conditions dry asphalt, asphalt covered with water, ice and snow the possibility of a preview sensor is exploited. By measuring the reflectance from the different surfaces with a halogen light and an actual sensor (Road eye) in a laboratory surroundings the advantage and disadvantage are revealed. The sensor is also mounted in a Volvo truck for real-life condition measurements.

The INTERSAFE project was created to generate an European approach to increase safety at intersections. A detailed accident analysis was carried out. Based on the derived relevant scenarios driver assistant functions are developed to support the driver in critical intersection situations. In addition evaluation and user test results of the Intersection Driver Warning System are presented and discussed.

This contribution presents an overview of a whole detection and tracking system designed for supporting future smart ACC applications. The key system features are the usage of the latest signal processing and tracking algorithms addressing the major problems of today’s available systems. The detection scheme based on sensor fusion of a multi-beam Lidar and a vision sensor is presented as well as a tracking algorithm fusing data from both sensors for refined state estimates and improved detection performance.

Lidar ACC Stop&Go is an advanced driver assistance system that was recently introduced. The system is capable to handle a large set of driving situations.

In this paper an extension to this driver assistance system using information from a digital map database is presented. The system behavior is improved and features are added by using an electronic horizon from the map data. Digital maps extend the range of the laserscanner with abstract information.

Many advanced driver assistant systems depend on a correct determination of the dynamic states of the host vehicle and of moving objects detected by environmental sensors. In order to estimate the correct position and orientation of tracked objects over ground, the movement of the host vehicle must be known precisely. The translation and the change of the yaw angle of the host vehicle during a short period of time are determined using integrated standard sensors, such as yaw rate, steering angle and wheel speed sensors. However, in extreme situations, such as skidding or wheel spin, the integrated sensors are not able to measure the host vehicle’s real translation and rotation. In order to provide a valid position and orientation information even in these extreme situations especially for object tracking systems, stationary objects in the environment of the vehicle are detected by an automotive laser scanner and are used as landmarks.

This paper brings an innovative approach to generate 3D scene views from real world data using a matrix range sensor and a 2D video sensor. Besides the enhanced visualization, combining 2D and 3D data provides also valuable information for object detection and recognition as well as for other image processing methods. The scope of this work includes a discussion on extracting good features from low resolution images, necessary for the system calibration. The registration process is based on a geometrical model which is derived from the calibration parameters of the cameras. Finally, a FPGAbased solution is provided, which generates the 3D scene on-the-fly. The proposed architecture achieves its maximum performance with a fully pipelined implementation from the raw data to the generated fusion coordinates, while limited hardware resources are consumed. In contrast to standard processor approaches, a significant performance boost is achieved with the FPGA architecture.