Small-scale self-driving cars: A systematic literature review

Abstract

The autonomous vehicle (AV) technology has the potential to significantly improve safety and efficiency of the transportation and logistics industry. Full-scale AV testing is limited by time, space, and cost, while simulation-based testing often lacks the necessary accuracy of AV and environmental modeling. In recent years, several initiatives have emerged to test autonomous software and hardware on scaled vehicles. This systematic literature review provides an overview of the literature surrounding small-scale self-driving cars, summarizing the current autonomous platforms deployed and focusing on the software and hardware developments in this field. The studies published in English-language journals or conference papers that present small-scale testing of self-driving cars were included. Web of Science, Scopus, Springer Link, Wiley, ACM Digital Library, and TRID databases were used for the literature search. The systematic literature search found 38 eligible studies. Research gaps in the reviewed papers were identified to provide guidance for future research. Some key takeaway emerging from this manuscript are: (i) there is a need to improve the models and neural network architectures used in autonomous driving systems, as most papers present only preliminary results; (ii) increasing datasets and sharing databases can help in developing more reliable control policies and reducing bias and variance in the training process; (iii) small-scaled vehicles to ensure safety is a major benefit, and incorporating data about unsafe driving behaviors and infrastructure problems can improve the accuracy of predictive models.