By Andreas von Eichhorn, Peter Zahn, Dieter Schramm (auth.), Jan Fischer-Wolfarth, Gereon Meyer (eds.)
The highway automobile of the longer term will include concepts from 3 significant car know-how fields: motive force information platforms, car networking and replacement propulsion. clever platforms corresponding to adaptive ICT parts and MEMS units, novel community architectures, built-in sensor structures, clever interfaces and useful fabrics shape the root of those good points and allow their winning and synergetic integration. They more and more seem to be the main allowing applied sciences for secure and eco-friendly street mobility. For greater than fifteen years the overseas discussion board on complicated Microsystems for automobile purposes (AMAA) has been profitable in detecting novel traits and in discussing the technological implications from early on. the subject of the AMAA 2013 should be “Smart structures for secure and eco-friendly Vehicles”. This publication comprises peer-reviewed papers written through top engineers and researchers which all tackle the continued examine and novel advancements within the field.
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Extra resources for Advanced Microsystems for Automotive Applications 2013: Smart Systems for Safe and Green Vehicles
SLAM is performed in the mapping and the localization module. DATMO is performed in the detection and the tracking module. The following section describes each module separately. Fig. 2 The figure shows how the laser sensor readings are processed and how the modules interact. 1 Localization Three localization modules with different complexities were implemented. Their appropriateness to the intended application in an outdoor environment turned out not to be equivalent. Since global localization doesn’t have to be achieved in precrash scenarios, there must be a tradeoff between computational complexity and accuracy.
New static sensor readings update the map and the location of the ego-vehicle simultaneously, while dynamic sensor readings are regarded as noise and left out . Also, vehicle odometry provides proprioceptive data to improve the location of the vehicle. The results of the SLAM process are a map and the location of the ego-vehicle in that map. In a A Flexible Environment Perception Framework 23 succeeding step, static objects are extracted from the raw occupation information in the map. Appropriate approximations must be made to reduce the computational complexity of solving both processes simultaneously.
A slight drift error relative to the DGPS pose over time does not affect the performance of the system, which only depends on the position relative to the local surrounding map and its quality. 2 Mapping Since feature-based localization proved not to be robust enough, only a occupancy grid map is used. From this map, it is possible to extract static objects by searching clusters of occupied cells. In occupancy grid maps the 2D space around the ego vehicle is divided into cells for which probabilities of occupation can be inferred via laser scanner measurements in combination with a probabilistic sensor model.
Advanced Microsystems for Automotive Applications 2013: Smart Systems for Safe and Green Vehicles by Andreas von Eichhorn, Peter Zahn, Dieter Schramm (auth.), Jan Fischer-Wolfarth, Gereon Meyer (eds.)