Accurate and reliable localisation

Localisation innovation programme

For instance, automated trailer systems in distribution centres require accurate manoeuvring capabilities to park trailers with centimetre-level precision in front of docking doors. Similarly, self-driving cars must reliably detect their lane to accurately interpret speed limits and define appropriate driving policies for manoeuvres like overtaking.

TNO's localisation innovation programme is exploring three types of solutions to create cost-efficient, state-of-the-art localisation systems and architectures.

1. Use of novel localisation sensor inputs

TNO is exploring innovative solutions to enhance localisation accuracy. Redundant sensor inputs can compensate for sensor weaknesses, especially in situations where GNSS satellite localisation for lane detection falls short. TNO is investigating matching map information with visual or radar sensor data as an alternative solution to improve accuracy. Additionally, TNO is exploring real-life applications of radio beacons based on Ultra Wide Band (UWB), Wifi, or 5G cellular technology.

2. Advanced fusion of localisation sensor data

At TNO, we are focused on optimising the fusion of information from multiple redundant sensors. We are exploring military grade fusion algorithms that leverage our system expertise to select the best fusion approach for each architecture and application, ensuring optimal utilisation of sensor data.

3. Online estimation of localisation integrity

Even with the combination of multiple sensors and optimal fusion algorithms, achieving 100% reliability in a localisation system is not guaranteed. TNO's localisation integrity innovations focus on designing solutions that estimate the likelihood of localisation errors while driving. If an automated vehicle requires the localisation error to be below a certain limit for safe operation, the automation function should be disabled if the likelihood of an unacceptable error exceeds a predefined threshold. This approach enhances the safety and reliability of automated driving systems.