Taking the "what-ifs" out of self-driving cars

   

The Simulation and Optimal Processes Group from Ilmenau University of Technology (TU Ilmenau) is just one of the exhibitors at the "Forschung für die Zukunft" R&D collaboration pavilion. The Group's contribution to the stand hosted by the German regions of Thuringia, Saxony and Saxony-Anhalt, is a model-based system that shows how it would roll out the concept of autonomous driving.

Audi Autonomous Driving Cup 2018

   

Hard work paid off after five months of great effort!

Cooperative autonomous vehicles management with learning-based algorithm in VANET

   

C2C and C2X information through wireless interface will be integrated for cooperative driving strategies in the traffic situations, such as urban intersections, highway ramp merging and traffic jams.... [click for more informations]

TU Ilmenau will present future technologies on the Hannover Messe

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We present autonomous driving with high-tech model cars (Audi Q2 on a scale of 1:8). The Audi Autonomous Driving Cup is an international competition aiming at developing fully automatic driving functions and corresponding software architecture, for which our Team AFILSOP won the 1st prize in 2017. Decided for our win is the application of a model-based optimal control alogrithm. Since our algorithm considers the passenger´s desire, road situation and car dynamics, the computed driving strategy is more reliable and comfortable in comparison to the conventional methods. The realized concept is industrial-oriented and can be easily transferred to real cars.

press release

Video Interview

Audi Autonomous Driving Cup 2017

   

[1st place] Team AFILSOP wins Audi Autonomous Driving Cup 2017

Theory: 3B methods for autonomous driving

   

Below is the Theoretical Approach.

Our practice sets are (click for details):

Goal: realization of autonomous driving

 
   

B1: Data-based (to identify current driving conditions, permissible range, etc.)

  • Laser scanner, radar
  • Camera
  • GPS
  • Communication between vehicles
 

B2: model-based (for predicting the driving dynamics in the next time moment)

  • Model equations
  • Calibration of the parameters
  • Description of the restriction of the permissible range
 

B3: algorithm-based (to calculate the driving strategy in real time)

  • Solution of an optimization problem
  • Computational efficiency
  • Convergence properties

Formulation of the optimization problem

 

Literature

   

Evgeniya Drozdova, Siegbert Hopfgarten, Evgeny Lazutkin, Pu Li
Autonomous Driving of a Mobile Robot Using a Combined Multiple-Shooting and Collocation Method
IAV 2016 - 9th IFAC Symposium on Intelligent Autonomous Vehicles, 29.06.2016 - 01.07.2016, Leipzig, Germany