  The rising popularity of self-driving cars has led to the emergence of a new
research field in the recent years: Autonomous racing. Researchers are
developing software and hardware for high performance race vehicles which aim
to operate autonomously on the edge of the vehicles limits: High speeds, high
accelerations, low reaction times, highly uncertain, dynamic and adversarial
environments. This paper represents the first holistic survey that covers the
research in the field of autonomous racing. We focus on the field of autonomous
racecars only and display the algorithms, methods and approaches that are used
in the fields of perception, planning and control as well as end-to-end
learning. Further, with an increasing number of autonomous racing competitions,
researchers now have access to a range of high performance platforms to test
and evaluate their autonomy algorithms. This survey presents a comprehensive
overview of the current autonomous racing platforms emphasizing both the
software-hardware co-evolution to the current stage. Finally, based on
additional discussion with leading researchers in the field we conclude with a
summary of open research challenges that will guide future researchers in this
field.
