Autonomous Driving (AD) has garnered significant attention in recent years across multiple domains. Despite notable advancements in algorithms and hardware, large-scale deployment of autonomous vehicles remains constrained by the lack of adequate real-time guarantees. This article comprehensively investigates real-time assurance challenges in AD systems from theoretical and practical perspectives. First, we introduce foundational concepts of real-time systems and analyze common modeling approaches, including the multi-rate DAG and processing chain DAG models. We then delve into the task scheduling and communication mechanisms of three representative middleware systems–ROS2, Cyber, and ERDOS–and the seL4 operating system. Our analysis reveals their underlying design philosophies and optimization strategies for real-time performance. The findings highlight the critical difficulty of meeting stringent real-time requirements in autonomous systems. By offering insights into current limitations and opportunities for improvement, this article aims to establish a deeper understanding of real-time assurance issues and encourage greater focus on system-level guarantees within the AD community.

This paper proposes a probabilistic amplitude shaping (PAS) scheme with systematic cascaded staircase and Hamming codes (CSHC). The integration of low-complexity concatenated codes within PAS and the use of the SIHO decoder are enabled by applying the inter-bit independence assumption. This ensures compatibility with high throughput transmission. In addition, the use of a reduced test pattern set in the SIHO decoder reduces the test pattern cardinality by 34.37%. The proposed scheme is experimentally demonstrated by self-homodyne coherent transmission over a 22.5 km 7-core fiber, with 21 wavelength division multiplexing (WDM) channels in the C-band is demonstrated. The total rate of the WDM-space division multiplexing (SDM) system is 21.77 Tbit/s. The experimental results show that the proposed PAS scheme with CSHC provides a gain of more than 8 dB in the back-to-back (BtB) scenario at the pre-FEC BER of 2.88E-2. Moreover, an average gain of 7.26 dB is achieved at the same threshold in WDM-SDM transmission.

Vehicle-Infrastructure Cooperative Autonomous Driving (CAD) is a new paradigm of autonomous driving, which relies on the cooperation between intelligent roads and autonomous vehicles. This paradigm has been shown to be safer and more efficient compared to the on-vehicle-only autonomous driving paradigm. Our real-world deployment data indicate that the effectiveness of Vehicle-Infrastructure CAD is constrained by the reliability and performance of commercial communication networks. This paper targets this exact problem and proposes Tentacles, a middleware to achieve high communication reliability between intelligent roads and autonomous vehicles, in the context of Vehicle-Infrastructure CAD. Specifically, Tentacles dynamically matches Vehicle-Infrastructure CAD applications and the underlying communication technologies based on varying communication performance and quality needs. Evaluation results confirm that Tentacles reduces deadline violations by more than 88%, significantly improving the reliability of Vehicle-Infrastructure CAD systems.

We demonstrate a probabilistic shaping (PS) four-dimensional (4D) modulation in self-homodyne coherent transmission system. The 4D modulation is based on inter-symbol amplitude translation (AT) to perform set partitioning. The distribution of constellation points after AT is optimized by de-DC. The parity bits produced by the AT are transmitted with the pilot tone by remapping. In addition, a soft decision for this 4D-PS signal is proposed. An experiment of self-homodyne coherent ultra-high order 4D signal transmission based on two cores of a 7-core fiber is demonstrated with a spectral efficiency of 16.37 bit/s/Hz. The 4D signals with soft decision can provide up to 0.78 bit/symbol and 1.85 bit/symbol gain compared to normal polarization division multiplexing signals and hard-decision 4D signals.