Real time decision making for autonomous driving vehicles

Abstract

In one embodiment, a method, apparatus, and system may predict behavior of environmental objects using machine learning at an autonomous driving vehicle (ADV). One or more yield / overtake decisions aremade with respect to one or more objects in the ADV's surrounding environment using a data processing architecture comprising at least a first, a second, and a third neural networks, the first, the second, and the third neural networks having been trained with a training data set. Driving signals are generated based at least in part on the yield / overtake decisions to control operations of the ADV.

Description

technical field [0001] Embodiments of the present disclosure generally relate to operating an autonomous vehicle. More specifically, embodiments of the present disclosure relate to the use of machine learning algorithms for decision making in controlling autonomous vehicles. Background technique [0002] Vehicles operating in an autonomous mode (eg, driverless) can relieve the occupants, especially the driver, from some driving-related duties. When operating in autonomous mode, the vehicle can navigate to various locations using on-board sensors, allowing the vehicle to drive with minimal human interaction or in some situations without any passengers. [0003] Making decisions about surrounding objects is one of the key parts of autonomous driving technology. One type of decision that is made is a yield / overtake decision for a moving object in the environment around an autonomous vehicle (ADV), whose path intersects the ADV's planned path. The yield / overtake decision is d...

AI Technical Summary

Problems solved by technology

It is also time consuming as decisions need to be made iteratively for each object

Images