Adaptive Minimal Latency In-Sequence Ordering for Multi-Channel Data Fusion in Autonomous Driving

Many data fusion approaches in autonomous driving assume or require data to arrive in an in-sequence order. However, this can generally not be guaranteed for systems with multiple sensors having different transmission and processing latencies. Additionally, latency itself is performance and safety-critical. To ensure safe and efficient driving, latency must be kept minimal. State-of-the-art approaches artificially delay incoming data in an effort to overcome the sequence issue, which drastically increases latency by waiting. Detailed a-priori information about the sensor and transmission characteristics is required, yet, out-of-sequence data cannot be prevented, leading to data loss. In this work, making statistical assumptions reasonable for autonomous driving sensor configurations, we propose an optimal solution providing both guaranteed upper bounds on the data loss and the induced delay. Based on these assumptions, our approach maintains the optimal latency required to ensure in-sequence data ordering. Additionally, by estimating sensor and system characteristics online, our method adaptively adjusts according to the current situation. We demonstrate the superiority of our approach with an extensive evaluation based on both simulated and real-world data. For the latter, using data from an autonomous vehicle emphasizes the importance of our work for intelligent vehicles.