By Andreas von Eichhorn, Peter Zahn, Dieter Schramm (auth.), Jan Fischer-Wolfarth, Gereon Meyer (eds.)
The highway automobile of the long run will embody concepts from 3 significant car know-how fields: driving force guidance structures, motor vehicle networking and substitute propulsion. clever platforms equivalent to adaptive ICT parts and MEMS units, novel community architectures, built-in sensor platforms, clever interfaces and practical fabrics shape the foundation of those positive aspects and allow their winning and synergetic integration. They more and more seem to be the main permitting 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 winning in detecting novel tendencies and in discussing the technological implications from early on. the subject of the AMAA 2013 should be “Smart platforms for secure and eco-friendly Vehicles”. This booklet comprises peer-reviewed papers written via best engineers and researchers which all deal with the continued examine and novel advancements within the field.
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Extra info for Advanced Microsystems for Automotive Applications 2013: Smart Systems for Safe and Green Vehicles
Then, DATMO is performed simultaneously with measurements from both scanners. Figure 2 shows an overview of the general framework architecture. The whole process is divided into four modules. For each module, several special types were implemented. Those that work best for the evaluated scenarios are presented. 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.
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. Occupancy grid maps are first introduced by . Occupancy grid maps store and combine the sensor readings on a low level.
The adequate out-of-sequence handling is crucial for time-critical applications such as pre-crash systems. Several methods are discussed and compared with respect to accuracy and computational costs. In addition, a reduced out-of-sequence algorithm for practical application is derived. The performance of the pre-crash system is evaluated using real-world data from crash tests. To this end, a soft crash target is used with a position ground truth accurate to the centimeter and a contact sensor as temporal ground truth.