What is autonomous trucking data collection?
Autonomous trucking data collection is the capture of long-range sensor data, scenario-targeted highway drives, and yard and dock recordings that train and validate self-driving Class 8 trucks and the automation around them. Heavy trucks need to see farther and plan earlier than cars, so the data has to cover distant objects, work zones, trailer states, and the hubs where freight starts and ends.
Driverless freight is already on public roads. In May 2025, Aurora began regular driverless customer deliveries between Dallas and Houston, after more than three million autonomous miles in supervised pilots and with sensors that see beyond the length of four football fields.
What that means for the rest of the industry: the highway middle mile is proving out. The open problems are at the edges: work zones, weather, and the yards and docks at each end of the trip, where robots, people, and trailers all mix.
How trucking differs from passenger-car autonomy
Our automotive page covers passenger ADAS and AVs. Trucking data has its own demands:
- Range: an 80,000-pound truck needs far more distance to stop than a car, so perception data has to be labeled and evaluated at much longer ranges.
- Articulation: the trailer moves differently from the tractor, and sensors see it in every turn.
- Hubs: freight starts and ends in yards and docks, which are unstructured, crowded, and rarely mapped.
- Loads: trailers arrive loaded in ways no one planned, which matters for unloading robots and inspection.
| Application | What the system must learn | Data we collect | Services |
|---|---|---|---|
| Highway perception at range | Debris, stalled vehicles, and pedestrians hundreds of meters out | Long-range LiDAR and camera drives with calibration and time sync | LiDAR, edge case |
| Work zones and incidents | Lane shifts, cones, flaggers, emergency vehicles | Scenario-targeted drives through active work zones by time and weather | LiDAR, edge case |
| Yard truck autonomy | Trailer hookup, tight maneuvers, people on foot | Yard runs across shifts, weather, and trailer types | LiDAR, edge case |
| Trailer loading and unloading | Mixed cases, collapsed stacks, floor-loaded freight | 3D scans of real trailer loads and teleoperated unloading | Teleop, point cloud |
| Trailer and load inspection | Damage, securement, seal checks | Camera and 3D capture at gates and docks | Multimodal, point cloud |
Hub-to-Hub Coverage: data for the whole freight trip
Gamasome framework
Most trucking datasets are heavy on highway miles. Freight doesn't start or end on the highway. Hub-to-Hub Coverage splits a trip into its segments and checks the data for each one.
A driverless truck that can't leave the yard is a very expensive parked truck.
Yard and gate
Tractor-trailer hookup, gate queues, people on foot, and tight turns, recorded across shifts.
On-ramps and merges
Heavy vehicle merges at highway speed, with long-range views of approaching traffic.
Highway at range
Small hazards at distance, labeled at the ranges stopping distance demands.
Work zones and incidents
Lane shifts, flaggers, and emergency scenes, which change weekly and must be captured as they appear.
Dock and unload
Docking, trailer state at opening, and load condition for unloading robots.
What a yard autonomy program looks like
Composite scenario: autonomous yard truck at a distribution center (details generalized)
- Situation
- An autonomous yard truck moves trailers between parking and dock doors at a busy distribution center.
- Problem
- It performed well on day shift but stopped often at night and in rain, and it hesitated when drivers walked between trailers to check seals.
- Solution
- Night and wet-weather yard runs with LiDAR and cameras, staged pedestrian scenarios with site staff under the yard's safety plan, and every run tagged by trailer type and lighting.
- Outcome
- Training data for the conditions causing stops, plus a held-out second yard to confirm improvements before expanding.
For the yard manager, the measure is moves per hour. Stops caused by missing night and rain data are the easiest ones to eliminate.
Common mistakes in trucking data
Labeling only to car ranges
If labels stop at car-relevant distances, the model never learns the far field a heavy truck needs.
Stale work zone data
Work zones change weekly. Collection should run continuously, not as a one-time campaign.
Highway-only datasets
Yards and docks are where many autonomy handoffs happen. Give them their own coverage.
Ignoring the trailer
Trailer swing and type change what sensors see. Tag trailer configuration on every run.
For unloading and dock robots, see our warehouse page. Scenario evaluation for safety cases runs through validation and testing.
Autonomous trucking data FAQs
What is autonomous trucking data collection?
It's the capture of long-range sensor data, scenario-targeted highway drives, and yard and dock recordings used to train and validate self-driving trucks, yard tractors, and the automation around freight hubs.
How is trucking data different from passenger-car data?
Heavy trucks need longer stopping distances, so perception must be labeled at longer ranges. Trailers add articulation, and freight trips include yards and docks that passenger-car datasets rarely cover.
Can you collect data in active work zones?
Yes, through scenario-targeted drives that follow work zones as they appear and change, tagged by layout, time, and weather.
Do you collect yard and dock data?
Yes. We record yard truck operations, trailer hookups, and dock approaches across shifts and weather, with pedestrian scenarios staged under the site's safety plan.
Can you help with trailer unloading robots?
Yes. We capture 3D scans of real trailer loads and teleoperated unloading demonstrations, including collapsed and floor-loaded freight.
Who has launched driverless trucking commercially?
Aurora began regular driverless customer deliveries between Dallas and Houston in May 2025, after more than three million autonomous miles in supervised pilots.