Lessons from Testing LiDAR and Audio Sensors in Harsh Environments

By Bo Redfearn

Let’s be honest, most sensor validation doesn’t happen in a pristine lab with perfect lighting and climate control. It happens in the real world, where conditions are messy, unpredictable, and sometimes just plain brutal.

When I was validating LiDAR and audio sensor systems for autonomous vehicles, we tested everything from dense fog to high heat. Here are a few things I’ve learned, sometimes the hard way.

Cold, Hot, and Totally Unpredictable

Sure, the specs say the gear works from -20°C to 70°C. But in reality, sensors can behave strangely under extreme conditions.

Cold boots delay signal stability
Heated components create unexpected IR reflections
Materials expand and shift, throwing off precise alignments

Takeaway: Always test like it’s going into the field.

Fog, Dust, Rain… the Usual Drama

One of the hardest parts of sensor validation is figuring out if a sensor is actually failing or if the environment is just making it look like it is.

Fog diffuses laser pulses, weakening returns
Wet, reflective surfaces like signs or puddles cause noisy reflections
Dirty lenses or enclosures lead to clean-looking but misleading results

Takeaway: Trust your tests, not just your sensors.

Ground Truth = Sanity Check

To make sure our LiDAR systems were returning meaningful data, we relied on precise ground truth tools to verify sensor accuracy. When you're trying to validate range and object detection, it helps to have a gold standard to compare your system against.

This was especially useful when comparing how sensors responded to different materials, reflective surfaces like road signs, absorbing materials like opaque surfaces, and even how two objects side by side could confuse returns without careful tuning.

Takeaway: Don’t just collect data, double-check it.

Validation ≠ Reliability

This is something I’ve seen teams get tripped up on.

Validation asks: “Does this work the way we designed it?”
Reliability asks: “Will this keep working over time, across edge cases, and under stress?”

You can pass validation and still fail in the real world, especially if your sensor isn’t tested against vibration, temperature cycling, or unpredictable materials.

Validation tells you it's ready for release.
Reliability tells you it's ready for reality.

Good Tools, Not Just Good Sensors

Sensor behavior becomes a lot easier to understand when you build the right tools around them. Visualization scripts, overlays, timestamp matching, those things save hours of digging.

Takeaway: Great sensors are important, but great tooling makes them so.

One Thing I Got Wrong Early On

I used to think validation was just about “making sure it works.”
Now I know it’s more about understanding how it fails, and how predictably.
Those aren’t the same thing.

Final Thought

You can’t control the weather, or fog, or heat, or wind. But you can design tests that prepare for them.

Sensor testing in harsh environments isn’t glamorous, but it’s how you make systems trustworthy. And honestly, there’s something satisfying about watching your tech thrive in chaos and still function like it’s supposed to.

Got your own sensor stories or test setups that worked better than expected?
Would love to swap notes, drop them below or shoot me a message.

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