Tag: Autonomous Driving Data Framework (ADDF)

In the realm of autonomous vehicles and advanced driver assistance systems (AV/ADAS), automotive original equipment manufacturers (OEMs), Tier 1 suppliers, and independent software vendors (ISVs) all process vast amounts of data recorded by test vehicles in the field. That recorded data can reach up to 300 TB per vehicle per day. When the recorded data is […]

In the robotics industry, Reinforcement Learning (RL) is extensively utilized to address complex problems that traditional path planning algorithms cannot manage, especially those involving intricate manipulations. The reward function in RL is a critical component that establishes the objective and directs the agent’s learning process. Crafting an effective reward function is often challenging but essential […]

Autonomous vehicles (AV) must be driven hundreds of millions of miles – and sometimes hundreds of billions of miles – to demonstrate their reliability in terms of fatalities and injuries. There is a need for alternative methods to supplement real-world testing including virtual testing and simulations, mathematic modeling and analysis, and scenario and behavior testing. In this […]