To help mitigate road fatalities due to human error, transportation stakeholders are turning to advanced driver assistance systems and autonomous vehicle (AV) development. However, the stakeholders continue to seek assurance of the safety performance of this new technology. This is often done using simulation testing of AV sensors and other platforms for simulation (microsimulation of AV movements, AV testing in a cab driving simulator, at AV test tracks, AV-dedicated road networks and in-service roads). Simulation is particularly important for the perception module of AV systems. Perception is a key module that typically uses light detection and ranging (LIDAR) sensors and enables efficient obstacle detection and environment mapping. To address this research need, this report reviews the various sensor technologies and explores their merits and limitations. This study focuses on sensor testing for AV operations using CARLA simulation. Extensive research on the use of LIDAR for autonomous driving has been documented in the literature, and researchers and practitioners have advocated for continued investigation of LIDAR placement alternatives. Next, the report developed a sensor placement evaluation framework. Given the numerous sensor placement criteria and location alternatives associated with the sensor placement, the study used multi-criteria decision analysis (MCDA). MCDA has been identified in the literature as an effective tool for decision making in various contexts of AV operations. However, its application in sensor placement optimization remains unexplored. In framework for evaluating the sensor placement alternatives, this study first established the placement alternatives and then developed a comprehensive, yet diverse set of evaluation criteria. The simulation equipment used is CARLA. For each alternative sensor placement design for AV operations, the weighted and scaled sensor evaluation criteria were amalgamated to generate the overall evaluation score. This enabled ranking of the placement designs and identification of the best design for purposes of AV operations. The findings of this study serve as a reference for future similar efforts that seek to optimize the placements of LIDAR or other sensor types based on an established set of sensor performance criteria. Further, it is expected that the study’s framework will contribute to enhanced understanding of the overall impact of the sensor placement on AVs, thus, enabling their cost-effective placement design and, ultimately, improving AV operations and outcomes including safety and mobility.