The impressive milestones achieved in the on-going global race for highly automated or autonomous vehicles are setting the stage for major transformations across the transportation industry, led primary by:

  1. The expected wide adoption of Level 2 and Level 3 automated driving systems in next-generation automotive vehicles.
  2. The forward-looking roadmaps of major technology vendors, predicting next generation consumer-centric fleets of robo-taxis.
  3. The growing maturity and expected proliferation of autonomous or Enchanted Flight Vision System (EFVS)-augmented Urban Air Mobility (UAM) scenarios that have proven their credentials as a safe, secure and efficient aviation transportation system for passengers and cargo operating at low altitudes within an urban and suburban areas.

Transforming however, these technological concepts into a tangible reality has been proven more challenging than originally anticipated, with recent studies urgently calling for a new-generation of integrated, cost-effective and multi-domain sensory systems, capable of providing to Machine Learning (ML)-based algorithms the required heterogenous environmental information in a low-energy envelope. In this context, Silicon Photonics based LiDAR and microwave-photonic RADAR implementations have been identified as key enabling technologies for next generation sensing systems, due to their cost, size and energy advantages, as well as their increased frequency operational bandwidth, which translate into high range resolution and light’s inherent diffractive capabilities. Delving deeper, however, into the recently demonstrated photonic-assisted LiDAR and RADAR prototypes reveals some fundamental shortcomings in their pathways towards next-generation high-performance yet cost- and power-efficient sensor solutions:

  • The well-established need for multi-beam beamforming capabilities in LiDAR and RADAR implementations is still lacking a solid development roadmap.
  • Distinct Sensor deployments are still dominating the photonic sensor-based landscape, despite the well-known synergies and complementarity of LiDAR and RADAR sensory data that can offer fail-safe and weather condition agnostic environmental data.
  • Data acquisition through photonic-assisted LiDARs and RADARs is still lacking a tight synergy with the data processing and interpretation domain.