Why low-cost LiDAR is opening the door to open-road testing

After three decades of autonomous vehicle testing, I’ve witnessed the evolution of the hardware used to gather data from simple GPS-based navigation to today’s sophisticated multi-sensor systems.

The integration of robust localisation technologies isn’t just solving deployment challenges—it’s fundamentally transforming how we test autonomous vehicles, enabling more reliable data, streamlined processes, and access to previously untestable environments.

For years, our testing programs were constrained by GNSS limitations. Urban canyons, underground facilities, tunnels, and dense tree canopies were either off-limits or required complex workarounds that compromised data integrity. We’d spend countless hours planning routes around these “dead zones,” missing critical real-world scenarios. 

Multi-sensor fusion has eliminated these constraints. The dramatic reduction in LiDAR costs, from over $8,000 just five years ago to under $2,000 now, has made comprehensive sensor fusion economically viable. Some manufacturers are even targeting sub-$500 price points, which could make multi-sensor fusion accessible in even more geographies. LiDAR Inertial Odometry systems are capable of maintaining centimetre-level accuracy even when GNSS signals are unavailable, expanding our testable environments by 40% and allowing validation in previously inaccessible scenarios. 

Multi-sensor fusion hasn’t just expanded where we can test—it’s dramatically improved test data reliability. Traditional single-sensor approaches created data gaps requiring extensive post-processing to correct. Now, tightly coupled GNSS/IMU systems integrated with LiDAR and camera data generate continuous, high-fidelity datasets with multiple independent validation streams.

This redundancy is invaluable for edge case validation. When one sensor encounters challenging conditions, the fusion system maintains accurate ground truth, allowing us to isolate individual sensor performance without losing system context. This has reduced our data validation time by 60% while improving confidence in results. 

Real-time, high-accuracy positioning enables dynamic test scenario generation previously impossible. We can now execute complex manoeuvres in confined spaces and validate navigation through construction zones. Crucially, we can initiate data logging before leaving the workshop and maintain continuous positioning through tunnel networks and when passing under oval test track infrastructure, creating seamless datasets without the data gaps that previously plagued validation efforts.

Modern multi-sensor fusion solutions integrate into existing test platforms without complete overhauls. By augmenting established GNSS/IMU systems with affordable LiDAR units and fusion algorithms, we’ve expanded our operational testing window from 60% to over 95% of intended scenarios at a fraction of the cost of developing new platforms. This leverages years of investment in existing infrastructure while dramatically extending capability into tunnels, covered facilities, and complex urban environments.

Upcoming Euro NCAP 2026 and CNCAP 2026 protocols demand testing methodologies that weren’t feasible with traditional localisation. These standards require validation in complex urban environments, tunnel transitions, and multi-level structures where GNSS reliability is compromised. Multi-sensor fusion provides the foundation for generating the high-quality test data these protocols demand. 

If you’re already using OXTS RT INS systems, you’re equipped with market-leading inertial navigation accuracy that forms the perfect foundation for advanced multi-sensor fusion. The RT range’s survey-grade precision provides the ideal platform to build upon. By integrating affordable LiDAR units with your existing RT system, you can access these transformative testing capabilities immediately, expanding your operational window, improving data reliability, and testing in previously inaccessible environments. The technology exists, costs have plummeted, and integration is straightforward. The question isn’t whether you can afford to upgrade your testing capabilities—it’s whether you can afford not to.