Understand data, signals and sensors which are behind modern autonomous driving functions in vehicles.

Sensors and corresponding signals are overwhelming the infrastructure of a next generation car. This course gives basic understanding on how to fetch and process signals, with emphasis on image and video. Through image processing, participants are introduced with advanced driver assistance systems and corresponding algorithm designs, including object detection, pedestrian and vehicle tracking, and 3D reconstruction of the scene.

Course topics:

• Background theory on signals

• Signals and real-time

• Fetch and store operations, memory and latency

• Introduction to advance driver assistance systems

• Introduction to image processing

• Image segmentation, edge detection, object detection

• Image analysis and object tracking

• Pedestrian and vehicle tracking algorithms

• Sensor fusion in automotive

• 3D reconstruction of a vehicle surroundings

• From signal to actuation

Hardware (required): Computer with Internet connection, working speakers and microphone.

Software: Chrome browser.

Course Typically Offered: Live Online in Fall quarter (mid June - August), Winter quarter (mid January - March), Spring quarter (April - mid June) or Summer quarter (mid June - August).

Prerequisites: Students should have basic engineering knowledge gained from either industry experience or appropriate level of undergraduate studies (ideally in electronics, mechanical or software engineering). Basic knowledge of digital signal processing is desireable. Ideally, students have completed the course NIT-AU-01: Next-Generation Vehicles and Architectures.

Next Step: To further practice and deepen the knowledge in automotive engineering, consider taking the remaining courses from the Automotive Engineering catalog (NIT-AU-XX).

Course Number: NIT-AU-02

Duration: 3.00 units (~30 live teaching hours, ~60 hours of individual practice and preparation work)