There is a growing interest in autonomous driving systems that can safely rely on multiple sensors including GNSS, IMU, Radar and LiDAR to navigate with high accuracy, integrity, continuity, and availability in complex urban environments. Many existing data sets, collected with multi-sensor platforms, focus on validating different variations of visual localization algorithms like SLAM, place recognition, object detection and visual odometry that help navigate in sky-obstructed and GNSS-denied environments. However, GNSS still plays a vital role in providing the most assured navigation solution. In this thesis, we develop a robust system intended for collecting data sets that will support the design of tightly integrated navigation algorithms and the analysis of integrity risk using GNSS coupled with IMU, Radar, and LiDAR in challenging automotive environments. GNSS pseudorange, doppler, and carrier phase and IMU acceleration and angular velocities are measurements that the system is specifically designed to collect for sensor-fusion algorithm refinement. In addition, time synchronization between sensors is crucial in data sets validating tightly integrated navigation, especially in applications with high dynamics. However, there is no widely accepted accurate and stable method for synchronizing clocks between different sensor types. We implement a common-clock synchronization and a hardware-trigger clock synchronization between multiple sensors. We then collect a preliminary data set to compare the accuracy and stability of sensor time-tagging using a GNSS-receiver-generated hardware trigger versus using a local-clock ROS-based time stamping. We evaluate the impact of these synchronization methods on mapping accuracy performance. / Master of Science / There is a growing interest in vehicles that can drive themselves without human intervention. Typically, these vehicles must rely on different types of sensors that perceive the environment in different ways and complement each other to navigate complex environments. Many algorithms have been developed to use the measurements from these sensors to accurately determine the vehicle position, velocity and orientation with high accuracy. Many existing data sets intended to validate these algorithms focus on sensors that use visual perception to navigate. In this thesis, we develop a robust data collection system to support (a) the validation of innovative navigation system design that make full use of complementary sensor properties and (b) the quantification of how much trust we can put into the navigation solution. In addition, tight integration of these sensors requires accurate timing of the measurements across multiple sensors. However, there is no widely accepted method of synchronizing clocks between multiple sensor types. We implement a first method in which all sensor information is time-stamped using a common clock, and a second method in which one sensor sends a pulse to another to synchronize their two clocks. To compare the accuracy and stability of these synchronization methods, we collect a preliminary data set.