The public housing built under the Franco regime in Spain (1939–1975) brought about the largest urban growth in the country’s recent history. It shares similar characteristics with other public housing built in Europe during the 20th century, and today it houses a predominantly multicultural and socioeconomically precarious population. This study analyses and evaluates three estates that make up a new urban axis in the city of Murcia. The objective is to evaluate the benefits and shortcomings of these developments in order to establish rehabilitation plans, which will seek to improve the lives of their inhabitants and enhance their experience of the urban area from environmental, functional and interactive perspectives. The methodology used for this study is based on the application of three systems of indicators in order to obtain a quantitative and qualitative evaluation of each neighborhood. The quantitative analysis evaluates the urban area in terms of environmental sustainability, while the two qualitative systems explore the neighborhoods’ functionality and ability to instil a sense of attachment, and therefore responsibility, among their inhabitants. In its conclusions, the article provides concrete lines of action for updating and regenerating these residential areas that form a significant part of the city of Murcia.

Autonomous driving in complex urban environments remains challenging due to perception uncertainty, dynamic multi-agent interactions, and control instability under adverse conditions. Despite advances in individual components, systematic evaluations of fully integrated modular pipelines under compounded urban disturbances remain scarce. This work presents a modular autonomous driving framework in CARLA Town10HD, integrating Convolutional Neural Network (CNN)-based perception using ResNet-18, global path planning via A* algorithm, and two control strategies: a classical Proportional–Integral–Derivative (PID) controller and a Deep Q-Network (DQN) agent with adaptive geometric steering assistance. A structured protocol assessed robustness across five scenarios: Heavy Rain, Dense Fog, Nighttime Driving, Dense Traffic, and Combined Extreme Conditions. The perception module achieved F1-scores close to 0.99 for traffic-sign, pedestrian, and lane classification; results reflect synthetic CARLA data and should not be interpreted as real-world generalization. The PID controller produced smoother trajectories with lower steering oscillations, while the DQN agent achieved faster traversal times at the cost of higher control variability. Route efficiency remained around 0.96 under isolated disturbances and decreased to 0.52 under compounded conditions, confirming sensitivity to multi-factor complexity. This study contributes a reproducible multi-scenario benchmark quantifying stability–adaptability trade-offs between classical and learning-based control, identifying scenario generalization and simulation-to-reality transfer as key future directions.