Perception sensors for CAM: AESIN publishes new report series

As part of its Perception Sensors series, AESIN, the Automotive Electronics Systems Innovation Network has introduced a new content series entitled “Perception Sensors for CAM, A Reality Check ”. This series brings together a collection of technical blogs and reports developed in collaboration with our partners from the Innovate UK Sim4CAMSens and Sim4CAMSens2 projects.

The series showcases how perception sensors are rigorously tested, analysed, and validated to ensure reliable performance in challenging environments.

The first of these reports, UK Automotive Perception Sensors Landscape and Recommendations, which can be accessed HERE, outlines the UK’s strengths, challenges, and opportunities in automotive perception sensors, which underpin Advanced Driver Assistance Systems (ADAS) and Connected and Automated Mobility (CAM).

The global automotive sensor market is expected to grow from £30.8 billion in 2023 to £62.2 billion by 2028. In the UK, ADAS could reach £3.2 billion by 2033, while CAM technologies may contribute up to £66 billion annually and support 38,000 jobs by 2040. The UK has strong expertise in radar, photonics, and quantum technologies but lacks domestic Tier 1 sensor manufacturers.

Radar is identified as the UK’s strongest growth opportunity, supported by active R&D and IP potential. Cameras are a mature area with strengths in AI and image processing, while LiDAR and GNSS/IMU offer longer-term opportunities through investment and collaboration. On-board compute remains a challenge due to limited UK industrial presence.

Key challenges include high development costs, limited manufacturing capability, the absence of Tier 1 suppliers, and regulatory gaps.

The report, written by AESIN’s Edlun Dcruz and Gunny Dhadyalla, recommends increased R&D funding, stronger support for commercialisation, development of domestic capabilities, and targeted investment in advanced technologies such as solid-state LiDAR, AI-enhanced radar, and quantum-enabled PNT.

(Picture: AESIN)

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