Enabling self-driving vehicles to efficiently and autonomously navigate through an obstacle-filled environment remains a topic of significant contemporary research interest. Motion-planning frameworks, encapsulating both path- and trajectory-planning, have played a dominant role in realizing the deployment of a “sense-think-act” intelligence for autonomous vehicles. However, verification and validation of such intelligence on actual self-driving autonomous vehicles has been limited. Simulation-based verification and validation has the advantage of permitting diverse scenario-based testing and comprehensive “what-if” analyses - but is ultimately limited by the simulation fidelity and realism. In contrast, testing on full-scale real-world systems is constrained by the usual challenges of time, space, and cost engendered in reproducing diverse scenarios in practice. Further, motion-planning frameworks often engender a mixture of global-planning (typically performed offline) coupled with a sensor-based local-planning (typically done online), which requires both simulation and physical testing.
Thus, scaled vehicle experimentation provides researchers with an exciting via-media to evaluate the performance and robustness of motion-planning algorithms on actual physical hardware - especially in real-time sensor-based motion planning settings. In this paper, we analyze a 1/10th scale F1/10 vehicle's performance in simulation and the actual hardware. A global planning algorithm is used to provide the waypoints for a feasible collision-free path between the start and goal configurations in the environment. We explored the deployment of Rapidly exploring Random Tree (RRT) and Rapidly exploring Random Tree* (RRT*). The Time Elastic Band local trajectory planner in ROS is then used for the realization of smooth, feasible paths between the waypoints. A comparison of validation in simulation has been provided with a detailed discussion of the parametric tuning for improving each case's performance.