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This Ph.D. thesis presents novel design, optimal tuning, and parallel real-time implementation of Tightly-Coupled (TC) Visual-Inertial Navigation (VIN) systems integrated with the Global Navigation Satellite System (GNSS) for autonomous vehicle applications. The Visual-Inertial-GNSS Navigation (VIGN) problem concerns estimating the position, velocity, and attitude of a mobile platform carrying only an on-board camera, an Inertial Measurement Unit (IMU), and a GNSS receiver. Although the cost and size efficiency of VIGN systems offer great commercialization potentials, achieving high accuracy, robustness, and real-time performance on embedded computing platforms are still challenging. Accuracy and robustness in VIGN systems heavily rely on two factors: first, a tightly-coupled fusion scheme that harvests the deep inter-modality correlations in the sensory data, and second, a well-tuned fusion model that sufficiently characterizes the actual behaviour of the VIGN system in practice. However, fusing multi-modal sensory data in the TC fashion scheme, as in the VIGN system, inevitably imposes a high computational burden due to the large state space and diverse volumes of visual processing, making it quite challenging to satisfying strict real-time constraints on embedded computing platforms. To address these challenges, this Ph.D. thesis proposes novel design and optimization approaches that have resulted in three main contributions. This Ph.D. research initially develops an enhanced VIN system based on Multi-State Constraint Kalman Filter (MSCKF). Further, it proposes a novel systematic design and automatic tuning framework to adjust its design parameters for optimal state estimation.Evolutionary techniques based on the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm have been employed to efficiently search in a high-