D-RISK: Validating the autonomous vehicle safety-case using simulation and adaptive test generation
Lead Participant:
DRISK.AI LIMITED
Abstract
"**Need.**
Simulation will be a vital component in the Connected Autonomous Vehicle (CAV) safety-case, enabling the validation and certification of autonomous Vehicle Control Systems (VCS). This is essential in cities where the density and diversity of other agents, including road-users, pedestrians, infrastructure and signage, as well as intermodal-connectivity, present a limitless set of potential scenarios that must be safely negotiated, either individually or collectively.
No existing simulation system enables a full safety-case approach to certification within an overarching risk architecture based on clearly-defined key performance indicators and areas. Limitations on incumbent physical simulators and models fail to capture:
* High complexity of edge-case scenarios in these contexts, which are difficult to predict and occur with low frequency compared to the majority of everyday driving scenarios
* Unpredictable responses of other road users and pedestrians to CAVs
* Rapidly changing technology and regulatory landscapes
* Relevant socioeconomic and human factors
Fundamentally, too many individual types and levels of risk exist for just one kind of continuous real-world simulation and corresponding simulation platform.
**Disruptive solution.**
In response, aiPod have brought together an ambitious cross-industry consortium, including Imperial College London, Claytex, DG Cities and Transport for London, to develop and integrate, as part of a simulator system, a novel platform-agnostic scenario generator for simulated testing of SAE Level-4/5 urban autonomy at the scale of the individual sensor, CAV and entire transport network.
Applicable to all potential CAV variants and any simulation architecture feeding a safety-case approach, the solution targets a step change in capability to:
1. Identify and structure complex realistic edge-case scenarios
2. Adaptively formulate the most relevant test routines to virtually-validate CAV decision-making performance
3. Qualify and quantify overall risk, aggregated across the relevant scenarios and parameter space
A multi-level framework targets unprecedented capability to identify complex edge-case test scenarios from multi-mode real and simulated data, human-driven fault-detection and public responses to CAV.
Overcoming limitations of existing simulators and models, the project targets a functional architecture that is inherently evolvable and extendable, whilst allowing constraints to test and certify specific deployments. Actionable feedback delivered by the solution and wider project can be leveraged to evolve the overall UK autonomous vehicle simulation capability and regulatory position."
Simulation will be a vital component in the Connected Autonomous Vehicle (CAV) safety-case, enabling the validation and certification of autonomous Vehicle Control Systems (VCS). This is essential in cities where the density and diversity of other agents, including road-users, pedestrians, infrastructure and signage, as well as intermodal-connectivity, present a limitless set of potential scenarios that must be safely negotiated, either individually or collectively.
No existing simulation system enables a full safety-case approach to certification within an overarching risk architecture based on clearly-defined key performance indicators and areas. Limitations on incumbent physical simulators and models fail to capture:
* High complexity of edge-case scenarios in these contexts, which are difficult to predict and occur with low frequency compared to the majority of everyday driving scenarios
* Unpredictable responses of other road users and pedestrians to CAVs
* Rapidly changing technology and regulatory landscapes
* Relevant socioeconomic and human factors
Fundamentally, too many individual types and levels of risk exist for just one kind of continuous real-world simulation and corresponding simulation platform.
**Disruptive solution.**
In response, aiPod have brought together an ambitious cross-industry consortium, including Imperial College London, Claytex, DG Cities and Transport for London, to develop and integrate, as part of a simulator system, a novel platform-agnostic scenario generator for simulated testing of SAE Level-4/5 urban autonomy at the scale of the individual sensor, CAV and entire transport network.
Applicable to all potential CAV variants and any simulation architecture feeding a safety-case approach, the solution targets a step change in capability to:
1. Identify and structure complex realistic edge-case scenarios
2. Adaptively formulate the most relevant test routines to virtually-validate CAV decision-making performance
3. Qualify and quantify overall risk, aggregated across the relevant scenarios and parameter space
A multi-level framework targets unprecedented capability to identify complex edge-case test scenarios from multi-mode real and simulated data, human-driven fault-detection and public responses to CAV.
Overcoming limitations of existing simulators and models, the project targets a functional architecture that is inherently evolvable and extendable, whilst allowing constraints to test and certify specific deployments. Actionable feedback delivered by the solution and wider project can be leveraged to evolve the overall UK autonomous vehicle simulation capability and regulatory position."
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| DRISK.AI LIMITED | £2,271,510 | £ 1,590,057 |
Participant |
||
| CLAYTEX SERVICES LIMITED | £435,104 | £ 304,573 |
| IMPERIAL COLLEGE LONDON | £784,417 | £ 784,417 |
| TRANSPORT FOR LONDON | £13,258 | £ 13,258 |
| DG CITIES LIMITED | £296,600 | £ 296,600 |
People |
ORCID iD |
| Chess Stetson (Project Manager) |