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May 21, 2024

Fleet response: Lending a helpful hand to Waymo’s autonomously driven vehicles

  • Technology
The Waymo Driver autonomously navigating SF, PHX, and LA without the help of fleet response.

The Waymo Driver autonomously navigating SF, PHX, and LA without the help of fleet response

The Waymo Driver autonomously navigates tens of thousands of rider-only miles across San Francisco, Phoenix, Los Angeles, and Austin daily. It can navigate common scenarios, like adhering to a crossing guard directing traffic, as well as more unique interactions like avoiding a swerving vehicle. As the Waymo Driver travels across town, it might contact fleet response for additional help. 

Much like phone-a-friend, when the Waymo vehicle encounters a particular situation on the road, the autonomous driver can reach out to a human fleet response agent for additional information to contextualize its environment. The Waymo Driver does not rely solely on the inputs it receives from the fleet response agent and it is in control of the vehicle at all times. As the Waymo Driver waits for input from fleet response, and even after receiving it, the Waymo Driver continues using available information to inform its decisions. This is important because, given the dynamic conditions on the road, the environment around the car can change, which either remedies the situation or influences how the Waymo Driver should proceed. In fact, the vast majority of such situations are resolved, without assistance, by the Waymo Driver.

An illustrative view of Waymo’s fleet response tooling which provides personal real-time insights into a 3D scene of where a Waymo vehicle is operating.
An illustrative view of Waymo’s fleet response tooling which provides personal real-time insights into a 3D scene of where a Waymo vehicle is operating.

A fleet response agent has a suite of tools to help them understand what a Waymo vehicle encounters on the road. For example, fleet response can view real-time feeds from the vehicle’s exterior cameras and a 3D graphical representation of what the car perceives around it. They can also rewind available feeds to understand the immediate scene better.

An illustrative view of what a fleet response agent sees when providing the Waymo Driver additional context as it navigates a scenario with an active emergency vehicle.

Fleet response and the Waymo Driver primarily communicate through questions and answers. For example, suppose a Waymo AV approaches a construction site with an atypical cone configuration indicating a lane shift or close. In that case, the Waymo Driver might contact a fleet response agent to confirm which lane the cones intend to close. 

An illustrative view of the guidance a fleet response agent provides a Waymo vehicle as it navigates a particularly complex scenario on a blocked, narrow street. Traditionally, the Waymo Driver doesn’t traverse private driveways. Fleet response provides the Waymo Driver guidance to make even more room to efficiently clear the street and make way for the truck.

In the most ambiguous situations, the Waymo Driver takes the lead, initiating requests through fleet response to optimize the driving path. Fleet response can influence the Waymo Driver's path, whether indirectly through indicating lane closures, explicitly requesting the AV use a particular lane, or, in the most complex scenarios, explicitly proposing a path for the vehicle to consider. The Waymo Driver evaluates the input from fleet response and independently remains in control of driving. This collaboration enhances the rider experience by efficiently guiding them to their destinations.

The Waymo Driver prioritizes the safest course of action in a given moment. In some situations, even with inputs from fleet response, the Waymo driver may come to a stop if it determines that is the safest course of action. Once the car comes to a stop, it can autonomously resume operations, and in other instances, it may require additional support from Waymo Roadside Assistance to retrieve the vehicle manually. 

As the Waymo Driver improves over time, it can solve more ambiguous scenarios independently and needs less help. But as with the rest of our operations, a helpful human is no more than a touch of a button away.