Jaguar Land Rover envisions an all-terrain autonomous car

Luxury carmaker Jaguar Land Rover (JLR) is developing an off-road self-driving SUVs under a 3.7 million Euro (Rs 29 crore) project called ‘Cortex’. JLR claims that it is utilising the world-first ‘5D’ technology, which is enabling level 4 and 5 off-road automation.

The Project will develop the technology through algorithm development, sensor optimisation and physical testing on off-road tracks in the UK. The University of Birmingham, with its research in radar and sensing for autonomous platforms and Myrtle AI, the machine learning experts, have also joined the project. The project was announced as part of ‘Innovate UK’s’ third round of Connected and Autonomous Vehicle Funding in March 2018.

The Cortex project will take self-driving cars off-road, ensuring that they it is fully capable in any weather condition - dirt, rain, ice, snow or fog. As part of the project, a ‘5D’ technique combining acoustic, video, radar, light detection and distance sensing (LiDAR) data live in real-time is being engineered. Access to this combined data improves the awareness of the environment the car is in. Utilising machine-learning enables the self-driving car to behave in an increasingly sophisticated way, allowing it to handle any weather condition on any terrain.

Chris Holmes, connected and autonomous vehicle research manager, Jaguar, said, “It’s important that we develop our self-driving vehicles with the same capability and performance that customers expect from all Jaguars and Land Rovers. Self-driving is an inevitability for the automotive industry and ensuring that our autonomous offering is the most enjoyable, capable and safest is what drives us to explore the boundaries of innovation. CORTEX gives us the opportunity to work with some fantastic partners whose expertise will help us realise this vision in the near future.”

This project forms a part of the company’s vision to make the self-driving car viable in the widest range of real-life, on and off-road driving environments and weather.

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