High-Def Maps Key to Automated Trucking


High-Definition maps form the navigational foundation for much of a self-driving vehicle’s core driving functions by providing an accurate, detailed representation of the road environment, according to an industry expert published on Trucks.com.

From the article:

Structurally speaking, all HD maps have three primary layers of information: First, the actual 3D representation of the road and its related features and furniture—think stop signs, traffic signals, lane markings, crosswalks, curb heights and so on. All of this is captured with centimeter-level accuracy. Second, an interpretive layer that tells an AV what each such sign, light and marking means. Generally, this is termed the “semantic” layer. And third, a vector layer that outlines the optimal drive paths, essentially providing “virtual rails” for the AV to follow. The quality, format and detail of the data may vary from provider to provider, but this basic architecture remains the same.

It’s almost axiomatic that efficient routing is mission critical for the trucking industry. Poor routing wastes time, and time is money. Traffic delays cost the industry upwards of $75 billion and weather-related delays cost companies an additional $2 to $4 billion. Route planning is, thus, one of the core functions ATs must perform. To help find the quickest path from point A to point B, most HD maps incorporate some form of traffic-flow data. Flow, however, only provides part of the picture. To really understand the road ahead, you need an additional layer of information—namely, the underlying traffic eventsthat cause changes in traffic flow. Here, HD maps have historically fallen short. At best, the big players have Waze-like data—basic incident feeds that lack both the accuracy and detail necessary to support autonomous path planning.

This data deficit is especially pronounced in light of the size and complexity of something like a Class 8 truck. Things like lane width, signal distances, turn angles, auxiliary lane presence, and height/weight/cargo restrictions can have a massive impact on a truck’s ability to operate safely. Only by understanding the root event—and the event’s impact on each of these variables—can an autonomous truck assess the driveability of a given section of road. To illustrate: If an autonomous truck only knows there’s a traffic slowdown (i.e., traffic flow), it may continue down its planned route if no faster alternatives are available. Even if the truck knows that a grizzly accident is causing the slowdown (i.e., the traffic event), it will likely stay the course. Time is money, right? Only if the autonomous truck knows the full picture—that this grizzly accident narrows the road such that a required turn is no longer possible (i.e., the traffic event and impact)—does it realize it must opt for the alternative. It’s important, therefore, that autonomous trucking companies and their clients, push their map providers to include this kind of event data or find providers that do.

Full article here.  

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