Mobility & Built Environment

Streetwise: Accelerating Automated Driving with advanced scenario-based safety validation

In cooperation with

Siemens, AVL, Daimler Truck

The future of Automated Driving depends on reliable safety validation of vehicles, to ensure they provide optimal safety in all traffic situations. TNO StreetWise is an advanced methodology for virtual simulation that optimally mimics real driving conditions, bringing safe Automated Driving one step closer.

With ever more advanced Automated Driving Functions (ADFs), safety validation becomes more important and complex. Given the high number of possible scenarios in which an ADF needs to be tested, virtual simulation – in addition to proving-ground and field testing – is essential to discover if safety requirements are met within a reasonable timeframe. To optimally calculate the safety risks, virtual simulations need to be based on realistic test cases and characteristics. Because TNO StreetWise uses public road driving data, the virtual test cases are representative for real-world scenarios. To ensure a safe implementation of Automated Driving (AD) technologies and to achieve a positive risk balance, TNO offers innovative risk quantification methods and scenario statistics.

Advanced safety methodology

Based on TNO’s extensive experience with vehicle safety assessment, the TNO StreetWise methodology addresses operational safety, which is an extension of functional safety (ISO26262) and safety of the intended functionality (ISO/DIS 21448:2021 SOTIF). [2] [4] [7]. After all, the automation technology takes over tasks from the human driver and should be prepared for all situations in the operational domain it is designed for (ODD). This advanced methodology is backed by a series of scientific articles.

Safer Automated Driving

Sensitivities of the tested systems are determined and quantified by combining systematic descriptions of the vehicle’s surroundings in scenarios and statistics, with systematic analysis and testing of the vehicle behaviour in such scenarios. By using statistical data from the exposure to different scenarios, one can use these simulations for risk quantification [2] [5]. By doing so, our industrial clients can make significant steps towards safer Automated Driving. Also authorities responsible for type approval can use TNO StreetWise’s quantified risk estimation to determine the safety of a vehicle.

Efficient and flexible pipeline

The TNO StreetWise pipeline automatically processes your driving data into activities, scenarios, parameters and statistics. New types of scenarios can be added quickly using the underlying framework. Python algorithms running in a cloud environment ensure flexibility and scalability. The pipeline is ready for connected vehicles as well (V2X, I2V). A web GUI provides easy access to the scenario database for extraction of OpenSCENARIO test cases, including the related Euro NCAP tests. Test automation is supported through an API, as successfully demonstrated with AVL ModelConnect, Vires VTD and Siemens Simcenter PreScan. Sensitive driving data can also be processed at the client’s premises.

Accelerating safety validation

Many of our partners are working on extensive field-testing, data collection and scenario detection for Automated Driving. By collaborating and securely exchanging scenarios and statistics using Secure Multi-Party Computation (MPC), TNO manages to accelerate safety validation for our partners. At the same time, this technology allows us to fine-tune data collection campaigns and benchmark the databases of our clients.

Valuable network

Our clients benefit from the extensive TNO network of partners. For example, we are a certified Euro NCAP test body and were leading the development of the Cyclist-AEB test protocol (CATS). TNO is also leading the way when it comes to Euro NCAP scenario databases. The list of partners in B2B and EU projects includes most of the CCAM community (e.g., SAKURA [1], NTU CETRAN (Singapore), AVL [9], Siemens, Itility, Magna [9], IDIADA [1], BMW [7], Volvo [7], Continental [7], ika [1] [7], Virtual Vehicle [9], and Vedecom).

More about this project

  1. Zlocki, A., Op den Camp, O., Arrúe, Á., Taniguchi, S., Umeda, M., Watanabe, S., and Antona-Makoshi, J., “Towards the Harmonization of Safety Assessment Methods of Automated Driving: SAKURA – SIP-adus - HEADSTART, White Paper” December 9, 2021.

  2. Op den Camp, O., van Montfort, S., Uittenbogaard, J., and Welten, J., “Cyclist Target and Test Setup for Evaluation of Cyclist-Autonomous Emergency Braking”, in International Journal of Automotive Technology, Vol. 18, No. 6, pp. 1085-1097, 2017. DOI: 10.1007/s12239−017−0106−5

  3. De Gelder, E., Manders, J., Grappiolo, C., Paardekooper, J.-P., Op den Camp, O., and De Schutter, B., "Real-World Scenario Mining for the Assessment of Automated Vehicles," 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020, pp. 1-8, DOI: 10.1109/ITSC45102.2020.9294652.
  4. De Gelder, E., Op den Camp, O., and de Boer, N., “Scenario Categories for the Assessment of Automated Vehicles – version 1.7”, Technical Report, Nanyang Technological University, Singapore: CETRAN, 2020.

  5. De Gelder, E., Elrofai, H., Khabbaz Saberi, A., Op den Camp, O., Paardekooper, J.-P., and De Schutter, B., “Risk Quantification for Automated Driving Systems in Real-World Driving Scenarios”, IEEE Access 9, vol. 9, pp. 168953-168970, 2021, DOI: 10.1109/ACCESS.2021.3136585.

  6. De Gelder, E., Paardekooper, J.-P., Khabbaz Saberi, A., Elrofai, H., Op den Camp, O., Kraines, S., Ploeg, J., De Schutter, B., "Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework," in IEEE Transactions on Intelligent Vehicles, vol. 7, no. 2, pp. 300-314, June 2022, DOI: 10.1109/TIV.2022.3144803.

  7. Elrofai, H., Paardekooper, J.-P., de Gelder, E.,  Kalisvaart, S., and Op den Camp, O., “Scenario-Based Safety Validation of Connected and Automated Driving.”, Vision Paper TNO, 2018. (pdf) .

  8. “ISO/TR 21934-1:2021 Road Vehicles — Prospective Safety Performance Assessment of Pre-Crash Technology by Virtual Simulation — Part 1: State-of-the-Art and General Method Overview.”
  9. Kalisvaart, S., Slavik, Z., and Op den Camp, O., “Using Scenarios in Safety Validation of Automated Systems.” In: Leitner, A., Watzenig, D., Ibanez-Guzman, J. (eds) Validation and Verification of Automated Systems. Springer, 2020, Cham.

  10. Van de Sluis, J., Op den Camp, O., Broos, J., Yalcinkaya, I., and de Gelder, E., “Describing I2V Communication in Scenarios for Simulation-Based Safety Assessment of Truck Platooning.” Electronics 10, 1 (2021),

  11. Paardekooper, J.-P., van Montfort, S., Manders, J., Goos, J., de Gelder, E., Op den Camp, O., Bracquemond, A., and Thiolon, G., “Automatic Identification of Critical Scenarios in a Public Dataset of 6000 km of Public-Road Driving”, in 26th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Eindhoven, 2019.
  12. Op den Camp, O., van de Sluis, J., de Gelder, E., and Yalcinkaya, I., “Generation of Tests for Safety Assessment of V2V Platooning Trucks”, in 27th ITS World Congress, Hamburg, 2021.

  • EU H2020 Hi-Drive (2021-2025): Large-scale pilot for prolonged automation without interruptions (continuous ODD). Data analysis, edge cases, simulation.
  • TKI StreetWise Plus (2021-2022): Extension of scenario database for multi-actor simulation, I2V messages and roadside data sources. With Siemens and Itility.
  • EU H2020 ARCADE (2018-2022): Coordination and support project for CCAM projects. Stakeholder workshops on common evaluation methodology (Nov 2020) and edge cases (May 2021).
  • EU H2020 Headstart (2018-2021): Scenario-based safety assessment methodology including V2X, localisation and cyber security. Detailed methods for scenario selection and allocation to test methods. Use case truck platooning.
  • TNO PUMAS (2018-2020): Multi-partner scenario database project with AVL, Magna, Siemens and a Japanese OEM. Proof of concept of sharing scenarios from diverse driving datasets for 1,000 hours of data. Including completeness metrics.
  • EU H2020 L3Pilot (2017-2021): Large-scale pilot for L3 automation. Data analysis and impact assessment for Jaguar Land Rover and Aptiv. Evaluation of lane-change detection.
  • EU ECSEL ENABLE-S3 (2016-2019): Virtual verification and validation of automated systems. Methodology, scenario working group, valet parking use case, image-guided surgery use case, dataset inventory.

Get inspired

4 resultaten, getoond 1 t/m 4

Objective safety rating of autonomous vehicles coming closer

12 January 2023

How will we soon be able to objectively determine whether a self-driving vehicle is safe or not? TNO scientist Erwin de Gelder’s research on the use of realistic driving scenarios in the safety validation of autonomous vehicles has made a significant contribution to this.

TNO and Torc Robotics collaborate to use real-world data for autonomous truck validation

10 November 2022

TNO announced a strategic collaboration with Torc Robotics, an independent subsidiary of Daimler Trucks. They are working together to substantiate the safety of self-driving trucks using scenario-based safety validation. TNO’s StreetWise, a safety validation methodology based on a real-world scenario database, provides a large collection of “driving events.” The methodology is designed to test and validate autonomous driving systems’ performance according to the latest safety requirements.

Virtual Assessment to enable safe Automated Driving

3 August 2021
Automated driving systems: TNO, Siemens DISW and Itility started a TKI project, StreetWise+, to demonstrate the use of simulations for safety assessment of automated driving systems.

Real-time signal from bicycle can reduce number of serious accidents

4 February 2021

Research done by TNO, Bosch and Shimano shows: additional communication improves car’s emergency brake system.