Ir al contenido principal

Sensor Fusion and Non-linear Filtering for Automotive Systems

Learn fundamental algorithms for sensor fusion and non-linear filtering with application to automotive perception systems.

Sensor Fusion and Non-linear Filtering for Automotive Systems

Hay una sesión disponible:

¡Ya se inscribieron 11,830! Una vez finalizada la sesión del curso, será archivado.
Comienza el Sep 15
Termina el Nov 9
8 semanas estimadas
10–20 horas por semana
Al ritmo del instructor
Dictado por un instructor según un cronograma
Gratis
Cambio opcional de categoría disponible

Sobre este curso

Omitir Sobre este curso

In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors.

The course is designed for students who seek to gain a solid understanding of Bayesian statistics and how to use it to fuse information from different sensors. We emphasize object positioning problems, but the studied techniques are applicable much more generally. The course contains a series of videos, quizzes and hand-on assignments where you get to implement many of the key techniques and build your own sensor fusion toolbox.

The course is self-contained, but we highly recommend that you also take the course ChM015x: Multi-target Tracking for Automotive Systems. Together, these courses give you an excellent foundation to tackle advanced problems related to perceiving the traffic situation around an autonomous vehicle using observations from a variety of different sensors, such as, radar, lidar and camera.

De un vistazo

  • Institución: ChalmersX
  • Tema:Ingeniería
  • Nivel:Advanced
  • Prerrequisitos:

    Mathematical statistics and MATLAB.

Lo que aprenderás

Omitir Lo que aprenderás
  • Basics of Bayesian statistics and recursive estimation theory
  • Describe and model common sensors, and their measurements
  • Compare typical motion models used for positioning, in order to know when to use them in practical problems
  • Describe the essential properties of the Kalman filter (KF) and apply it on linear state space models
  • Implement key nonlinear filters in Matlab, in order to solve problems with nonlinear motion and/or sensor models
  • Select a suitable filter method by analysing the properties and requirements in an application

Plan de estudios

Omitir Plan de estudios

Section 1 - Introduction and Primer in statistics
Section 2 - Bayesian statistics (Week 1)
Section 3 - State space models and optimal filters (Week 1)
Section 4 - The Kalman filter and its properties (Week 2-3)
Section 5 - Motion and measurements models (Week 2-3)
Section 6 - Non-linear filtering (Week 4)
Section 7 - Particle filter (Week 5)

Testimonios de los estudiantes

Omitir Testimonios de los estudiantes

I found the course very useful and also very exciting. The content and structure of the course was very well planned. The way the concepts were explained was also very understandable and engaging. I learnt a lot during the course and will be using it a lot in my own work. I look forward to similar courses being offered in future.

Acerca de los instructores

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

¿Te interesa este curso para tu negocio o equipo?

Capacita a tus empleados en los temas más solicitados con edX para Negocios.