Measurement for our planet
Measurement for our planet

Following COP26, discover how NPL plays a key role in enabling climate action through the delivery of accurate, reliable data that supports decision making and enables low carbon innovation.

Case studies

Improving safety in automated vehicles

New sensor modelling framework will facilitate standardisation in performance testing

Case study

The challenge

To ensure safety within the automated vehicle industry, adequate testing needs to be carried out to understand the performance of systems in different circumstances. Virtual testing is a key part of this, and industry-wide standardisation of testing techniques is required so that a common language can be developed regarding performance.

Standardisation in virtual testing requires an understanding of the accuracy of these models, which in turn requires that their uncertainties are reliably evaluated. Sensor models must also be incorporated into the different testing and simulation standards, and so requirements need to be put in place for sensor models as well.

As the UK’s NMI, NPL will play an important role in the measurement and modelling of these virtual environments and sensor models. This includes ensuring that the industry understands the importance of proper uncertainty quantification to generate high quality testing results.

The solution

NPL is building a sensor modelling framework to facilitate a standardised practice for sensor model design in the automated navigation stack. It is hoped that this framework will become the common language for modelling automated system sensors.

The framework includes the following steps:

Scoping exercise: We list the requirements for the sensor model, including which phenomena to model and operational design domain (ODD)

  1. Deciding on model and data: We decide what data is required to capture the required phenomena and what model would be adequate according to the scoping exercise.
  2. Deciding on method of training: We select a method for training and optimising the model by fitting the parameters so that the phenomena are modelled as required by the scoping exercise.
  3. Deciding on method for uncertainty quantification: We then explore how to properly quantify uncertainty in the model. This is an important component as it can then be used to understand the uncertainty in the results of the simulation.
  4. Model training and evaluation: Based on the above steps we train and evaluate the model.

These steps could be used to create a sensor model and they should, with proper feedback and research, eventually evolve into steps that could be followed by the industry as a standard practice.

We also demonstrated the use of the sensor modelling framework when creating simple models for camera and Lidar sensors. These illustrate the steps in the framework and, with the help of industrial feedback, could be evolved to be used in automated driving systems and virtual testing.

The impact

The sensor modelling framework is a step towards enabling an interface between a standardised testing environment and the different industrial automated driving systems.

Such standardisation across the industry could result in a common language which would improve collaborative research. It could result in the development of high-quality testing standards and improved communication between industry and suppliers. These testing standards would ultimately impact system quality and robustness and result in improvements to the safety performance of automated vehicles. 

Find out more

Recent reports

A sensor modelling framework for autonomous systems 

Sensor modelling is an important aspect of developing and testing automated vehicles. This document describes a standardised framework for the practice of developing such models. We describe a general approach and illustrate it with examples for different sensors and sensor models.

Download report

Camera and Lidar sensor models for autonomous vehicles

Camera and Lidar are important sensors in the automated vehicle sensing stack. In order to ensure safety of automated vehicles we need to understand the working of these sensors and verify their functioning in different edge cases so we can understand their characteristics in the operational design domain (ODD). In this study we will demonstrate how to apply the sensor modelling framework to model the functioning of these sensors.

Download report