Occupancy maps are used in automotive driving applications to understand the scene around the vehicle using data from sensors like LiDAR and/or radar on vehicles. In state-of-the-art work, pattern-coupled sparse Bayesian learning (PCSBL) was used to estimate the occupancy map by leveraging spatial dependencies across grids in the map for both single modalities and the fusion of multiple modalities. The PCSBL method, however, has high computational complexity, making real-time implementation challenging for large-scale grid maps. To address this limitation, we propose several methods to improve the computational efficiency of PCSBL while maintaining mapping accuracy. First, we utilize a precomputed lookup table to accelerate selection matrix construction. Second, we implement adaptive resolution reduction based on sensor measurements. Third, we develop two novel methods that exploit the narrow angular interactions between measurements and the map regions to enhance computational efficiency. The first method partitions measurements into spatially disjoint submaps that enable parallel processing. The second method exploits the angular structure to impose a block structure on the selection matrix, reducing computational overhead. Experiments on the nuScenes and RADIATE public datasets show that the presented methods reduce computational costs compared to the benchmark PCSBL and fusionbased PCSBL methods while preserving detection accuracy.

With the continuous advancement of autonomous driving technology, the precision and efficiency of perception systems have become increasingly critical. Among various sensors, LiDAR plays a central role, and solid-state optical phased arrays (OPAs) are widely regarded as a promising future direction. However, traditional uniform OPAs often face challenges such as high power consumption and limited scalability.

This thesis addresses the design and optimization of sparse non-uniform OPAs, aiming to balance trade-offs among the number of antennas, element spacing, beamwidth, and side lobe level. We propose a novel formulation that simultaneously considers array sparsity and performance while enforcing distance constraints, which is solved using a modified genetic algorithm. The simulation results reveal a clear trade-off between sparsity and array performance, while also offering practical solutions to the constraints faced by current LiDAR systems. Furthermore, we investigate the impact of array configuration on beam steering and introduce a mathematical transformation that reformulates the steering design problem to be compatible with our model. The comparison results demonstrate that the proposed approach significantly improves array performance across the entire steering range.

Occupancy grid mapping is a method of representing the environment and its obstacles and drivable areas in a discretised grid that is usually constructed with point cloud data from sensors such as radars. These maps facilitate path planning and decision-making in autonomous driving applications, making them a crucial component. This thesis addressed the problem of dynamic occupancy grid mapping, which additionally models moving objects. The aim was to jointly model the spatial structure and temporal dynamics of the environment. Building on an existing sparse Bayesian learning framework exploiting sparsity and spatial structures, we proposed a dynamic extension that integrates radar range-rate measurements into a motion prediction module. This module predicted the future positions of moving objects directly from the point cloud and incorporated them into the Bayesian inference process by altering the Gamma hyperprior, enabling better tracking of moving objects. The proposed method was evaluated on the real-world View-of-Delft dataset containing urban driving scenarios and compared against three other benchmark methods. Experimental results demonstrated superior detection of moving objects and improved shape reconstruction without significantly increasing false positives.

Occupancy grid mapping represents the surrounding environment with a discretized grid, providing information about obstacles and the drivable region using sensors such as LiDAR or radar. For automotive driving applications, these maps are central to safe autonomous navigation. While both model-driven and deep learning-based approaches exist, this thesis develops a hybrid method to estimate the occupancy grid map from point cloud data. Specifically, the proposed method builds on the pattern-coupled sparse Bayesian learning (PC-SBL) algorithm, which is well suited to the block-sparse, spatially correlated structure of automotive grids. By replacing explicit parameter updates with a lightweight convolutional neural network, the spatial correlations and sparsity profile are learned directly from the data. Based on qualitative and quantitative evaluation on LiDAR point cloud data from the nuScenes dataset, we show that the proposed approach surpasses the strong PC-SBL baseline in both accuracy and runtime. Moreover, when applied without further training to LiDAR and radar point clouds from the RADIATE dataset, it marginally outperforms PC-SBL, indicating robust cross-dataset and cross-sensor generalization.

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.