Blog – Auro

Creating Human-Like Driving Behaviors in Traffic Simulations, using real world data distributions

This article is a follow-up of a previously published blog post titled as “Accelerated Development of Learned Agents for Urban Autonomous Driving” (please see [1]). It deepens the explanation of our behavior-based actor controller that reproduces the human-like behaviors. This actor controller is mainly intended for reproducing learning scenes in driving simulators from which our […]

Accelerated Development of Learned Agents for Urban Autonomous Driving

Driving in an urban environment requires robust decision-making in complex situations. We often find ourselves turning at an intersection, yielding to a pedestrian, while allowing oncoming traffic to pass through. Sometimes we have to change lanes into a tight spot knowing that the vehicle behind us is going to slow down. At other times, we […]

Learning to Drive with Unity ML-Agents – A Beginners Guide to Deep RL for Autonomous Vehicles

Introduction This article is the first in a series devoted to the topic of designing an agent capable of learning to do a task well, with a particular emphasis on reinforcement learning (RL) for autonomous vehicles. We find that the task of driving is quite challenging, providing a rich case study to consider when discussing […]

Using Open Source Frameworks in Autonomous Vehicle Development – Part 2

In part 1 of this series we introduced a bridge between the ubiquitous ROS framework, and the newer Apollo Cyber RT, which was created for the Apollo autonomous driving platform. In part 2, we are happy to open-source a bridge between the popular Carla simulation platform and Apollo.

A Modular, Vehicle-Agnostic Sensor Housing Unit for Autonomous Vehicle Platforms

As the mobility and autonomous vehicle industry transition from a period of hyper-growth to a more consolidated phase, it is more important than ever to focus on near-term, real-world applications of L4 autonomy. We, at Auro, the Autonomous Driving Division of Ridecell, are focusing our efforts on low-speed L4 empty vehicle automation for high-yield shared […]

Using Open Source Frameworks in Autonomous Vehicle Development -Part 1

The open-source community for Autonomous Vehicle technology has been picking up steam recently, with Apollo and Autoware teams being the lead contributors. With aggressive timelines cooling down (Tesla being an exception) and with many industry experts advocating that L4 autonomy at scale, though inevitable will take it’s time to go mainstream, an open-source autonomous vehicle […]

Since its acquisition in 2017, Auro is now the autonomous driving division of Ridecell.