Another fantastic accolade for the dRISK team and our work in the Autonomous Vehicle sector. Katarina Cimesa presented dRISKs products and services at this years Autonomous Mobility World Expo (AMWE) 2023 in Paris and came away winning the Startup Challenge award! Now in its 5th year, the Autonomy Mobility Startup Challenge – formerly known as […]
The UK government is awarding almost £42 million to 7 projects through the Centre for Connected and Autonomous Vehicles (CCAV) Commercialising Connected and Automated Mobility (CAM) competition. Industry consortia will match the public grant to around £81 million and will be expected to demonstrate a sustainable commercial service by 2025. dRISK play a pivotal and […]
dRISK are delighted to have been featured in the Business Cloud Transport Tech top 50 innovators of 2022. The company has been recognised as creating innovative transport technology solutions for our work testing, training and validating on Edge cases gathered from all over the globe and it is a true recognition of the work the […]
dRISK were awarded Edge innovator of the year 2022 at the Edge Computing World conference in Santa Clara, Silicon Valley. Our Transport Systems specialist, Kiran Jesudasan collected the award in person and gave the audience a view of the work dRISK are pushing toward in the relatively new world of Edge computing. Kiran described how […]
….in NVIDIA DRIVE Sim The future of autonomous vehicles (AVS) relies on simulation. Equally important is knowing what scenarios to simulate. Edge cases are high-risk scenarios, each of which is individually unlikely, but collectively, can make up all the risk of daily driving. Edge cases can be anything from a child following a ball into […]
How can simulation be used to determine where ADAS features can be improved? The prestigious Institute of engineering and technology (IET) contacted dRISK to ask us help them test Advanced Driver Assistance Systems (ADAS). ADAS features are becoming more common place on the new vehicles, these include Automatic Emergancy Braking (AEB), Automatic Lane Keeping System […]
AI Engineer. Federico completed his MSc in Artificial Intelligence at the University of Edinburgh in 2021, with an emphasis on Deep Learning applied to Natural Language Processing and Computer Vision. His dissertation focused on developing an Unsupervised Synthetic Data Generation architecture with Unity, to generate synthetic data that closely resembles a set of real data, and can be used to train Computer Vision models. Prior to this, he earned an MSc in Mechanical and Industrial Engineering at Arts et Métiers ParisTech in France.
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