This paper considers autonomous navigation incrowded city environments. An autonomous vehicle testbed is presented. We address two challenges of pedestrian detection and GPS-based localization in the presence of high-level buildings. First, we augment the localization using local laser maps and show improved results. A pedestrian detection algorithm using a complementary vision and laser system is proposed. We implement this algorithm in our testbed and evaluate its performance.We also show how utilizing existing infrastructural sensors can improve the performance of the system. Potential applications of this work include fully automated vehicle systems in urban environments typical in megacities in Asia.