Autonomous and collaborative vehicles not only are seen as a possible solution to reducing congestion and traffic related fatalities. They also provide an excellent multi-domain test bench for engineering education at undergraduate and graduate level. Yet, the use of real scale platforms for experimental educational activities bears prohibitive costs and complexity. While several small scale autonomous platforms have been developed in recent years to address this issue, still they require a significant investment of time and money, which is not always ideal for undergraduate education. Furthermore, none of the available platforms are specifically developed for platooning experiments. In this paper, we detail the results of an undergraduate student's project where a pair of relatively low-cost, off-the-shelf small scale RC cars have been used to implement and test a well known platooning algorithm from the literature. Furthermore, a Virtual Potential Field (VPF) based lateral controller has been included in order to allow the cars to navigate a prescribed closed-circuit track. Self-location of each car has been obtained via a ceiling-mounted motion capture system. Results have shown that, even using a relatively low sampling rate of 10 Hz, accuracies in the order of 1 cm can be obtained when platooning at 0.5 m/s along a circuit of 4 by 3 m. As further improvements to the platform, apart from higher sampling rates for the control law, the inclusion of onboard perception is being explored, in order to eliminate the need for an external motion capture system.