H2020Doctoral network2020–2024

I-SPOT · Intelligent Ultra Low-Power Signal Processing for Automotive

Horizon 2020 — Marie Skłodowska-Curie Actions

Duration
2020-11-01 → 2024-10-31
EU contribution
€512,640
Participants
2
Scheme
MSCA-ITN

Lines connect the coordinator with its partners.

Results in brief

Intelligent Ultra Low-Power Signal Processing for Automotive

While traditional cars relied solely on the senses of their drivers – mostly their eyes and ears –, the car of today is equipped with more and more sensors. Self-driving cars come within reach by equipping cars with their own set of eyes, in the form of LIDARs and cameras. Yet, to become truly aware of their environment, such smart cars are still missing an invaluable input on which we, as humans, rely strongly: the acoustic information. I-SPOT targets the addition of acoustic sensing technology to cars, to bring enhanced environmental awareness. This acoustic information complements the information from other sensory technologies. In active (drive) mode, this will give information about nearby emergency vehicles, accidents, passing cars, etc., which are currently causing major disruptions of autonomous and computer assisted driving. Moreover, information on weather conditions, or mechanical car wear or failure is present in the acoustic signal. More than just detection of the nature of the sound, also the direction can be derived, and this even for visually occluded sources. In passive (park) mode, information can be obtained on car damaging, theft, or nearby critical events (e.g. cry for help). The acoustic sensor can as such form the low-cost wake-up trigger to direct the more power-hungry camera system to activate and point in a specific direction.

Data: CORDIS, © European Union

Project objective

Smart autos need to process and react to different types of stimuli from the surrounding environment. This data sensing and processing is performed with the support of different sensors, gathered data and the proper hardware platform to process the received data and react properly to the simuli whenever required. This project targets audio stimuli and considers two important challenges: Processing and localisation of the Audio signal based on the classic as well as deep learning methods

Original text from CORDIS.

Participants

  • KATHOLIEKE UNIVERSITEIT LEUVEN · LeuvenCoordinatorBelgium
  • ROBERT BOSCH GMBH · Gerlingen-SchillerhoeheGermany

Links

Data: CORDIS, © European Union