Autonomous Driving Testbed

DeepNNCar Testbed

To implement, validate, and test our research products, we have mainly used the CARLA simulator and the DeepNNCar. While CARLA is a well-known open-source urban driving simulator, DeepNNCar is a low-cost research testbed that was designed in the Smart and Resilient Computing for Physical Environments Lab (SCOPE). DeepNNCar is built upon the chassis of Traxxas Slash 2WD 1/10 Scale RC car and is mounted with a USB forward-looking camera, IR- optocoupler, and a 2D LIDAR. The speed and steer for the robot are controlled using pulse-width modulation (PWM), by varying the duty cycle. [Recommended Reading] [Web Content]

Demonstration

A demonstration of the DeepNNCar operating on an indoor track. The car performs end-to-end driving using a modified NVIDIA DAVE-II convolutional neural network. The network is trained to drive within the tracks while achieving high speeds. You can learn more about the platform from our GitHub

Shreyas Ramakrishna
Shreyas Ramakrishna
Senior Architect, System Safety Engineer