Automated vehicle technology for public roads

Thema:: Connected and cooperative; Smart traffic and transport

Automated driving technology is steadily progressing, with promising applications being rolled out across various sectors. Hub-to-hub autonomous trucking offers significant economic potential. Level 3 self-driving passenger cars enable drivers to use travel time more effectively. And automated public transport systems promise higher availability at lower costs.

Current challenges

However, significant challenges related to safety, reliability, and regulatory frameworks still hinder the large-scale implementation of autonomous driving.

Before widespread adoption of self-driving vehicles can be considered, it's essential to ensure they are completely reliable and capable of handling all possible road scenarios.

Our vision

Through the development of components and architectures that prioritise safety by design, our aim is to enhance the overall safety of the entire vehicle.
It's critical to utilise all available information that a vehicle can gather about its driving environment and internal state, including reliability estimates, to enhance the safety of self-driving vehicles.

Our solutions

Reliable localisation

The advancement of automated driving relies on reliable localisation, which can be achieved through the use of multiple new sensor inputs, sophisticated sensor signal fusion techniques, and online estimation of localisation integrity. TNO offers proven, beyond the state-of-the-art technology building blocks that enhance the capabilities of self-driving systems to accurately navigate diverse road environments, improving overall safety and reliability.

Context aware motion planning

Managing the complexity of the environment for autonomous driving systems requires a scalable and reliable methodology for specifying vehicle behaviour and translation into motion planning while considering all contextual factors. TNO is addressing combinatorial challenges by leveraging machine reasoning techniques to navigate diverse scenarios effectively and make informed decisions in real time.

Quality and trust of digital infrastructure

External data inputs, such as map information, road works warnings, or traffic light status, are essential for safe automated driving. To incorporate this information into safety-critical decisions, it's crucial to assess the reliability, accuracy, and security of the data.

TNO is actively addressing critical cyber security and trust issues within the automotive industry by collaborating with partners across the supply chain to develop effective solutions. Additionally, TNO is exploring solutions to verify and enhance the accuracy of information derived from the digital infrastructure.

Road condition estimation

Humans have a natural ability to detect and adapt to slippery road conditions, which can pose challenges for automated vehicles to replicate. At TNO, we are focused on developing sophisticated data analytics systems and algorithms capable of detecting changes in road surface conditions. Our goal is to enable automated vehicles to respond appropriately to these conditions, maintaining control and ensuring safety on the road.