Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. Accurate localization is essential for self driving vehicle navigation in GPS inaccessible environments. This thesis concentrates on low speed autonomous shuttles that are mainly utilized for university campus intelligent transportation systems and presents initial results of ongoing work on developing solutions to the localization and perception challenges of a university planned pilot deployment orientated application. The paper treats autonomous driving with real time kinematics GPS (Global Positioning Systems) with an inertial measurement unit (IMU), combined with simultaneous localization and mapping (SLAM) with threedimensional light detection and ranging (LIDAR) sensor, which provides solutions to scenarios where GPS is not available or a lower cost and hence lower accuracy GPS is desirable. The in-house automated low speed electric vehicle from the Automated Driving Lab is used in experimental evaluation and verification. An improved version of Hector SLAM was implemented on ROS and compared with high resolution GPS aided localization framework in the same hardware architecture. The overall configuration that combines ROS with DSpace controller can be easily transplantable prototype in other hardware architectures for future similar research. Real-world experiments that are reported here have been conducted in a small test area close to the Ohio State University AV pilot test route. are used for demonstrating the feasibility and robustness of this approach to developing and evaluating low speed autonomous shuttle localization and perception algorithms for control and decision making.