This seminar considers nonlinear estimation problems arising in automotive safety systems and system identification.
Information about the host vehicle and its surroundings, such as the position of other vehicles and the road geometry, are of paramount importance for active safety systems such as adaptive cruise control, collision avoidance and lane guidance. This information is obtained by solving a nonlinear estimation problem, based on appropriate motion models and measurements from radar, vision, inertial sensors, etc. This problem is also known as the sensor fusion problem. The performance of the proposed solution is demonstrated using sensor data from authentic traffic environments.
The second part of the seminar is concerned with parameter estimation of a relatively general class of nonlinear dynamic systems. A Maximum Likelihood (ML) framework is employed in the interest of statistical efficiency, and it is illustrated how an Expectation Maximization (EM) algorithm may be used to compute these ML estimates. An essential ingredient is the employment of so-called particle smoothing methods to compute required conditional expectations via a Monte Carlo approach. A simulation example demonstrates the efficacy of these techniques.
For more information regarding the research please have a look at: www.control.isy.liu.se/~schon/ .
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