TY  - ABST
AU  - Sergio Silva
AU  - Victor Campmany
AU  - Laura Sellart
AU  - Juan Carlos Moure
AU  - Antoni Espinosa
AU  - David Vazquez
AU  - Antonio Lopez
A2  - PUMPS
PY  - 2015//
TI  - Autonomous GPU-based Driving
T2  - PUMPS
BT  - Programming and Tunning Massive Parallel Systems
KW  - Autonomous Driving
KW  - ADAS
KW  - CUDA
N2  - Human factors cause most driving accidents; this is why nowadays is common to hear about autonomous driving as an alternative. Autonomous driving will not only increase safety, but also will develop a system of cooperative self-driving cars that will reduce pollution and congestion. Furthermore, it will provide more freedom to handicapped people, elderly or kids.Autonomous Driving requires perceiving and understanding the vehicle environment (e.g., road, traffic signs, pedestrians, vehicles) using sensors (e.g., cameras, lidars, sonars, and radars), selflocalization (requiring GPS, inertial sensors and visual localization in precise maps), controlling the vehicle and planning the routes. These algorithms require high computation capability, and thanks to NVIDIA GPU acceleration this starts to become feasible.NVIDIA® is developing a new platform for boosting the Autonomous Driving capabilities that is able of managing the vehicle via CAN-Bus: the Drive™ PX. It has 8 ARM cores with dual accelerated Tegra® X1 chips. It has 12 synchronized camera inputs for 360º vehicle perception, 4G and Wi-Fi capabilities allowing vehicle communications and GPS and inertial sensors inputs for self-localization.Our research group has been selected for testing Drive™ PX. Accordingly, we are developing a Drive™ PX based autonomous car. Currently, we are porting our previous CPU based algorithms (e.g., Lane Departure Warning, Collision Warning, Automatic Cruise Control, Pedestrian Protection, or Semantic Segmentation) for running in the GPU.
L1  - http://refbase.cvc.uab.es/files/SCS2015.pdf
N1  - ADAS; 600.076; 600.082; 600.085
ID  - Sergio Silva2015
ER  -