Occupancy grid maps provide information about obstacles and available free space in the environment and are crucial in automotive driving applications. An occupancy map is constructed using point cloud data from sensor modalities such as light detection and ranging (LiDAR) and radar used for automotive perception. In this article, we formulate the problem of estimating the occupancy grid map using sensor point cloud data as a sparse binary occupancy value reconstruction problem. Our proposed occupancy grid estimation method, based on pattern-coupled sparse Bayesian learning (PC-SBL), leverages the sparsity and spatial dependencies inherent in occupancy maps typically encountered in automotive scenarios. The proposed method shows enhanced detection capabilities compared to two benchmark methods based on performance evaluation with scenes from the nuScenes and RADIal public datasets.@en

Providing automated granular control of lighting, along with user-driven control, results in an energy-efficient smart lighting system design while catering to personal occupant preferences. Two functional ingredients in such a system are: 1) sensing that provides granular information on occupant location and 2) a communication system to transmit control messages from a user. In this paper, we consider an ultrasonic circular array sensor that provides the dual functionality of granular occupant sensing and a communication receiver for user control transmissions. A ceiling-mounted sensor configuration with a colocated ultrasonic transmitter and array receiver is considered. To perform presence sensing, this transmitter sends periodic bursts of sinusoidal pulses that, upon reflection from the environment, are received at the array sensor. The echoes are processed to obtain estimates of range, azimuth, and elevation angles corresponding to possible occupant movements. A Kalman filter based on a near constant velocity model is used for target tracking. The resulting occupant location is used for energy-efficient lighting control. A user may in addition control lamps in its vicinity by sending messages at ultrasonic frequency, which are processed by the receiver array, and used to further adapt requested parameters of the lighting system. The proposed sensing and messaging solution is tested in an indoor office space with an eight-element receiver array sensor prototype.@en

We consider the problem of detecting sensor commissioning in the form of determining the sensor layout. We address this problem for single-pixel thermopile sensors, located at the ceiling, that provide remote temperature measurements for people counting applications and HVAC controls. We employ a random forest classifier to determine the deployed layout in an area. For this classifier, we propose spatio-temporal distance features using two-sided cumulative sum recursive least squares (CUSUM RLS) filtering of the thermopile temperature sensor signals. Using sensor data generated with simulated occupancy patterns and a thermopile signal model, we show that the proposed method achieves a true positive rate (determining the correct layout) of 90.2% and false positive rate of 1.3%.@en