Benodigde kennis

De benodigde kennis wordt in samenwerking bepaald tijdens werkbijeenkomsten en Ronde Tafels. Dit leidt tot kennisvragen die worden beheerd door het Ministerie van Infrastructuur en Milieu (DGB).

De bovenstaande vragen zijn al deels beantwoord. De antwoorden staan in de documenten uit de collectie en zijn hier verzameld. (stand per maart 2017) Voor meer vragen en antwoorden: zie het Kennisjaarverslag.

TECHNICAL-Architectuur

100% Ja dat kan; 

Learning to Drive: Perception for Autonomous Cars Chapter 3 smooth road detection

‘’Accurate perception is a principal challenge of autonomous off-road driving. Perceptive technologies generally focus on obstacle avoidance. However, at high speed, terrain roughness is also important to control shock the vehicle experiences. The accuracy required to detect rough terrain is significantly greater than that necessary for obstacle avoidance. self-supervised learning approach for estimating the roughness of outdoor terrain. Our main application is the detection of small discontinuities that are likely to create significant shock for a high-speed robotic vehicle. By slowing, the vehicle can reduce the shock it experiences. Estimating roughness demands the detection of very small surface discontinuities – often a few centimeters. Thus the problem is significantly more challenging than finding obstacles.’’ Experimental results from this paper show that speed control – reduction  used offers significant improvement in shock  detection.

80% Self-supervised Road Detection in Desert Terrain

The paper presents  a  method  for  identifying  drivable  surfaces   in   difficult   unpaved   and   offroad   terrain   conditions   as encountered in the DARPA Grand Challenge robot race. Instead of relying on a static, pre-computed road appearance model, this method  adjusts  its  model  to  changing   environments.  It  achieves robustness  by  combining  sensor  information  from  a  laser  range finder,  a  pose  estimation  system  and  a  color  camera.  Using  the first  two  modalities,  the  system  first  identifies  a  nearby  patch of  drivable  surface.  Computer  Vision  then  takes  this  patch  and uses  it  to  construct  appearance  models  to  find  drivable  surface outward  into  the  far  range.  This  information  is  put  into  a  drivability  map  for  the  vehicle  path  planner.

50%

Do Autonomous Vehicles Learn?

Self learning capabilities of autonomous vehicles. The paper discusses discuss why, whether, and with which challenges and approaches machine learning is possible in its current form in autonomous driving. The paper portrays the view of vehicle technology in particular on this question, and is based on experience from the literature for the area of machine learning.

60%

Model Based Vehicle Detection and Tracking for Autonomous Urban Driving

This paper describes the moving vehicle detection and tracking module that we developed for our autonomous driving robot Junior. The module provides reliable detection and tracking of moving vehicles from a high-speed moving platform using laser range finders. The paper presents the notion of motion evidence, which allows us to overcome the low signal-to-noise ratio that arises during rapid detection of moving vehicles in noisy urban environments.


Hieronder een tabel met een aantal documenten die enkel betrekking hebben op het subdomein Technical-Architectuur. Er zijn nog meer documenten gerelateerd aan dit subdomein maar deze zijn ook gelinkt aan andere subdomeinen. De volledige collectie beschikbare documenten in onze online bibliotheek (catalogus en dropbox) 

Titel/Document Author(s)/Schrijvers By Date
Key Considerations in the Development of Driving Automation Systems Andy Christensen, Nissan – North America
Andrew Cunningham, Volkswagen (VW) Group of America Jerry Engelman, Ford Motor Company
Charles Green, General Motors
Charles Kawashima, Mercedes-Benz
Steve Kiger, CAMP
Danil Prokhorov, Toyota Motor Engineering & Manufacturing North America, Inc. Levasseur Tellis, Ford Motor Company
Barbara Wendling, Volkswagen (VW) Group of America
Frank Barickman, National Highway Traffic Safety Administration 06-10-15
An Introduction to Autonomous Control Systems Panos Antsaklis, Kevin Passino, J. Wang Supported in part by the Jet Propulsion Laboratory’ ??/06/1991
Self-supervised Road Detection in Desert Terrain Hendrik Dahlkamp, Sebastian Thrun Stanford University, Intel Corporation ??/??/2006
Real-time motion planning for agile autonomous vehicles Frazzoli, E. University of Illinois at Urbana / MIT ??/02/2015
Learning to Drive: Perception for Autonomous Cars David Stavens Stanford University ??/05/2010
Model Based Vehicle Detection and Tracking for Autonomous Urban Driving Anna Petrovskaya, Sebastian Thrun Stanford University 01-04-2009
Do Autonomous Vehicles Learn? Walther Wachenfeld, Hermann Winner Springer Berlin Heidelberg 22/05/2016