3D perception poses a significant challenge in Intelligent Transportation Systems (ITS) due to occlusion and limited field of view. The necessity for real-time processing and alignment with existing traffic infrastructure compounds these limitations. To counter these issues, this work introduces a novel multi-camera Bird-Eye View (BEV) occupancy detection framework. This approach leverages multi-camera setups to overcome occlusion and field-of-view limitations while employing BEV occupancy to simplify the 3D perception task, ensuring critical information is retained. A noble dataset for BEV Occupancy detection, encompassing diverse scenes and varying camera configurations, was created using the CARLA simulator. Subsequent extensive evaluation of various Multiview occupancy detection models showcased the critical roles of scene diversity and occupancy grid resolution in enhancing model performance. A structured framework that complements the generated data is proposed for data collection in the real world. The trained model is validated against real-world conditions to ensure its practical application, demonstrating the influence of robust dataset design in refining ITS perception systems. This contributes to significant advancements in traffic management, safety, and operational efficiency.

Recommendation systems provide recommendations based on user behavior andcontent data. User behavior and content data are fed to machine learning algorithms to train them and give recommendations to the users. These algorithms need a large amount of data for a reasonable conversion rate. But for small applications, the available amount of data is minimal, leading to high recommendation aberrations. Also, when an existing large scaled application with a high amount of available data uses a new recommendation system, it requires some time and testing to decide which recommendation algorithm is best suited to get higher conversion rates. This learning curve costs highly when the user base and data size are significantly high. In this thesis, A/B testing is used with manual intervention in the decision-making of recommendation systems. To understand the effectiveness of the recommendations, user interaction data is compared to compare experiences. Based on the comparisons, the experiments conclude the effectiveness of A/B testing for the recommendation system.