This thesis presents the development of a sensor fusion framework that integrates ultrasonic sensors with a rotating Light Detection and Ranging (LiDAR) system to generate an occupancy grid map. The objective is to improve spatial awareness for autonomous navigation by employing an adaptive LiDAR approach, wherein ultrasonic sensors are used to identify regions of interest for focused scanning.

The design and implementation of a test system are described, along with the development of an occupancy grid map capable of representing data from both LiDAR and ultrasonic sensors. To enhance the accuracy and reliability of the environmental representation, the occupancy grid map incorporates an inverse sensor model in combination with Bayesian statistical methods.

This thesis presents a novel approach based on Joint Deep Probabilistic Subsampling with Cram´er-Rao Lower Bound Integration (J-DPSC) for sparse antenna array design in distributed radar systems. The method addresses the critical challenge of achieving high angular resolution in autonomous driving applications while maintaining low hardware complexity. The proposed J-DPSC framework extends Deep Probabilistic Subsampling with theoretical foundations based on worst-case dual-target Cram´er-Rao Lower Bound analysis. This enables simultaneous optimization of both transmitter and receiver arrays across mono-static, bi-static, and joint operational modes. A Neighborhood Masking Mechanism ensures physical realizability of the designed arrays. Extensive simulations validate that our proposed sparse arrays achieve performance comparable to fully populated arrays while using significantly fewer elements. The results demonstrate peak sidelobe levels up to −10 dB with minimal resolution sacrifice (≤ 0.1 degree) for two-sensor distributed radar networks. The approach allows extension to three-sensor configurations for potential applications with multi-node networks. Monte Carlo evaluations across varying SNR levels and source (i.e., targets) separations show consistent superiority over random selection methods.

Summarizing, this work advances sparse array design methodology by bridging machine learning techniques with estimation theory, enabling a more generalized and flexible design tool for distributed radar networks in next-generation autonomous driving applications.

This thesis investigates a method to dynamically adapt the angular resolution of a 2D spinning lidar (Light Detection and Ranging) using an ultrasound sensor. The ultrasound sensor data is used to locate areas where a higher angular resolution is desired compared to other regions. It proposes two distinct methods to locate these areas based on real problems encountered in the autonomous automotive industry and assign numerical scores to areas of interest. These scores are then taken into account when deciding the angular resolution for that area. The adaptive algorithm is meant to increase the amount of data available on object of interest for use in object avoidance and trajectory mapping systems in autonomous vehicles. The thesis will cover the design, development and testing of the whole system.

This thesis addresses the design and optimization of sparse non-uniform optical phased arrays (OPAs) for advanced automotive LiDAR systems. As autonomous driving technologies advance, the demand for high-resolution, reliable, and compact LiDAR systems has become increasingly critical. Traditional uniform OPAs, while effective, face limitations regarding power consumption. This work introduces an innovative approach to designing sparse non-uniform OPAs that achieve desired performance metrics essential for automotive applications, including beamwidth, field of view, and sidelobe levels, while minimizing element count and, consequently, energy consumption.

Through mathematical modelling and simulation, we formulate the problem of sparse OPA design as an optimization problem, leveraging techniques from compressive sensing to identify the most efficient element arrangements. We propose using the sparse array synthesis method to formulate the sparse OPA design problem, utilizing algorithms such as LASSO, thresholding, and iterative reweighted l1-norm minimization to achieve optimal sparse configurations. Our results demonstrate substantial improvements in effectiveness, offering a practical solution to the constraints posed by current LiDAR systems. This thesis contributes to the field by providing a comprehensive framework for the design of sparse non-uniform OPAs, highlighting the trade-offs and benefits of various design strategies. The findings advance our understanding of OPA design principles.

Occupancy grid maps are fundamental to autonomous driving algorithms, offering insights into obstacle distribution and free space within an environment. These maps are used for safe navigation and decision-making in self-driving applications, forming a crucial component of the automotive perception framework. An occupancy map is a discretized representation of a chosen environment that is constructed using point cloud information obtained from sensor modalities like LiDAR and radar. In this project, we formulate the problem of estimating the occupancy grid map using sensor point cloud data as a sparse binary occupancy value reconstruction problem. We utilize the inherent sparsity of occupancy grid maps commonly encountered in automotive scenarios. Besides, the spatial dependencies between the grid cells are exploited to provide a better reconstruction of the boundaries of the objects inside the range of the map and to suppress the false alarms emerging from the reflections coming from the road. To address sparsity and spatial correlation jointly, we propose an occupancy grid estimation method that is based on pattern-coupled sparse Bayesian learning. The proposed method shows enhanced detection capabilities compared to two benchmark methods, based on qualitative and quantitative performance evaluation with scenes from the automotive datasets nuScenes and RADIal.