Light detection and ranging (LiDAR) plays a crucial role in machine perception for advanced driver assistance systems. Existing LiDARs, however, do not adapt their sensing strategy to complement driver's perception. We demonstrate a novel LiDAR prototype that dynamically adapts its range and resolution over the field of view, according to real-time driver gaze. Our gaze-aware LiDAR emphasizes scanning peripheral zones the driver may overlook, i.e., critical areas during driving. Our demonstration showcases enhanced perception, highlighting the potential of hybrid human-machine sensing for safer driving.

When designing wireless sensor networks (WSNs), energy efficiency and security are two crucial aspects to consider. The deployment of low-cost battery-operated sensors and limited communication bandwidths require the networks to be energy efficient. Meanwhile, the distributed nature of WSNs necessitates the networks to be secure. In this chapter, we consider the effect of Byzantine attacks, one of the most important security threats faced by WSNs, on one kind of promising energy-efficient decision-making schemes, called ordered transmission-based (OT-based) schemes. In energy-efficient OT-based schemes, less informative data is omitted from transmission for energy efficiency. Due to intermittent data reception from the sensors, the fusion center cannot fully learn the behavior of all the sensors. The problem of how Byzantine attacks affect the decision-making performance of energy-efficient OT-based schemes is investigated, and some possible countermeasures are discussed.