The recent developments in the research area of autonomous driving and mixed-criticality systems highlight a significant shift towards enhancing communication efficiency, collaboration robustness, and real-time decision-making capabilities. Innovations are particularly focused on optimizing the utilization of limited computational and communication resources, improving the accuracy and reliability of vehicle-to-everything (V2X) communications, and ensuring the safety and efficiency of autonomous driving through advanced control schemes and cooperative perception frameworks. These advancements aim to address the challenges posed by bandwidth constraints, calibration errors, and the need for real-time collaboration among vehicles and other agents in dynamic environments.

Noteworthy papers include:

• AXI-REALM: Introduces a lightweight, modular extension to AXI4 interconnects for real-time workloads, significantly improving fairness and execution predictability in mixed-criticality systems.
• Select2Drive: Proposes a framework that optimizes computational and communication resources for real-time collaborative autonomous driving, demonstrating significant improvements in perception tasks and driving performance.
• mmCooper: Presents a multi-agent, multi-stage cooperative perception framework that enhances communication efficiency and collaboration robustness, validated through extensive experiments.
• PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X: Develops a reinforcement learning-based control scheme for on-ramp merging, integrating enhanced C-V2X communication to improve safety and reliability.