  This paper introduces an approach that enhances the state estimator for
high-speed autonomous race cars, addressing challenges from unreliable
measurements, localization failures, and computing resource management. The
proposed robust localization system utilizes a Bayesian-based probabilistic
approach to evaluate multimodal measurements, ensuring the use of credible data
for accurate and reliable localization, even in harsh racing conditions. To
tackle potential localization failures, we present a resilient navigation
system which enables the race car to continue track-following by leveraging
direct perception information in planning and execution, ensuring continuous
performance despite localization disruptions. In addition, efficient computing
is critical to avoid overload and system failure. Hence, we optimize computing
resources using an efficient LiDAR-based state estimation method. Leveraging
CUDA programming and GPU acceleration, we perform nearest points search and
covariance computation efficiently, overcoming CPU bottlenecks. Simulation and
real-world tests validate the system's performance and resilience. The proposed
approach successfully recovers from failures, effectively preventing accidents
and ensuring safety of the car.
