COLA: Characterizing and Optimizing the Tail Latency for Safe Level-4 Autonomous Vehicle Systems

Autonomous vehicles (AVs) systems are envisioned to revolutionize our life by providing safe, relaxing, and convenient ground transportation. To ensure safety, AV systems need to make timely driving decisions in response to complicated and highly dynamic real-world driving environments. We present a systematic study to understand the causes of tail latency in AV systems and their impact on safety. We empirically analyze the design of two open-source industrial AV systems, Baidu Apollo and Autoware. We explore how pipelined computation design (such as module dependency and execution patterns), traffic factors (surrounding environments of AV), and system factors (such as cache contention) impact AV systems' tail latency. Inspired by the insights, We propose a set of systematic designs that lead to performance and safety improvements of up to $1.65 \times$ and $14 \times$, respectively.