Ignite, OST drive to solve autonomous vehicle challenges with AI

As regulatory authorities in the UK open applications for operators to run autonomous taxis, buses and private-hire cars, scalable software-defined vehicles (SDVs) venture Ignite by Forvia Hella has announced a collaboration with Samsung Electronics-owned artificial intelligence (AI) software firm Oxford Semantic Technologies (OST) to create an “explainable” AI service to help prove road safety compliance for autonomous vehicles (AVs).

The partners noted that their partnership comes as leading autonomous vehicle providers face challenges as their vehicles continue to deal with complex decision-making, resulting in, in some cases, vehicles driving into flooded roads.

In addition, the partners say that while driving performance is improving in AVs, manufacturers still lack robust ways to prove safety, compliance and decision logic at scale. This, they insist, has created a blocker on progressing to higher levels of autonomy, with manufacturers struggling to move from Level 2 (partial driving automation where the driver is legally responsible) to Level 3 (conditional driving automation where the manufacturer assumes liability) and Level 4 (fully autonomous driving).

The collaboration will aim to address the industry’s struggle to progress from Level 2 to Level 4 autonomy by providing ways to prove safety, compliance and decision logic at scale. It will also look to provide software engineers with a “white box” so they can understand how AI makes decisions, to improve safety features and seek approval with hard evidence.

For autonomous vehicles, knowledge-based AI can be used as the vehicle’s rulebook and memory – capturing everything the car does in real time, cross-referencing it against traffic rules, and ensuring every decision it makes is logical, traceable and compliant.

This provides software engineers with a white box of data they can use to better understand the AI that powers AVs, and improves safety features and performance. It also has the potential to produce evidence for regulators on how AVs make decisions in different circumstances and conditions, and follow road traffic rules.

From a technological standpoint, the simulation-based software uses OST’s RDFox knowledge graph database to provide a reasoning layer to AV systems, improving decision-making in complex situations. The simulation-based software is attributed with bridging the gap between traffic laws and live autonomous decision-making.

With its AI-centric engine, RDFox OST currently collaborates with organisations across Europe, Asia and North America. From integrating data across organisations to autonomous decisions and recommendations, OST’s technology is deployed in financial services, automotive, manufacturing, healthcare, publishing and retail.

The collaboration is also claimed to demonstrate how knowledge-based AI, which uses carefully curated expert knowledge and logical reasoning to solve complex problems, can be applied to the AV sector.

Unlike machine learning, which finds patterns in vast datasets and draws statistical outputs, knowledge-based AI aims to improve the accuracy of results by making logical and explainable decisions based on data combined with expert knowledge.

“Hella Ignite.Drive applies knowledge-based AI by translating traffic laws, originally written for human interpretation, into machine-readable rule sets,” said Felix Kortmann, chief technology officer at Ignite by Forvia Hella. “This enables manufacturers to generate deterministic evidence that demonstrates safe and compliant vehicle behaviour for European type approval.

“At the same time, it reduces development lead times by minimising the need for manual, market-by-market rule coding, helping AV teams move faster toward approval-ready deployment,” he said.

Oxford Semantic Technologies CEO Peter Crocker said: “Autonomous vehicles currently use AI to make a whole range of decisions on the road – but at the moment, manufacturers are struggling to show why or how these decisions are made. RDFox can help with this major barrier to progress. What knowledge-based AI allows us to do is collect and map these decisions and apply reasoning. We can see exactly why a vehicle acts in a certain way and use this data to help the vehicle make better decisions in the future.”

Ian Horrocks, Oxford University professor and OST co-founder, added: “The AV case study is a great example of how knowledge-based AI can enhance data-driven systems. A key advantage of the technology is traceability, where decisions can be linked back to the rules and logic that produced them. In the automotive space, this visibility can revolutionise go-to-market strategies, improving the compliance and safety of AVs.”