Sep 2026· IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems· Vol 45, pp. 4518-4531· 0 citations· 68 references
Abstract
Autonomous driving vehicles (ADVs) are transforming urban mobility with advanced sensors for real-time decision-making, promising safer and more efficient transportation. Despite recent advancements targeting accident reduction and efficiency improvement, challenges like sensor range limitations persist. Edge-assisted perception, facilitated by vehicle-to-everything (V2X) communications, addresses these limitations by sharing data among ADVs, enhancing accuracy in complex driving scenarios. However, this approach amplifies real-time computing challenges due to wireless communication-induced suspensions. This work presents a solution called MCS3 a suspension-aware mixed-criticality system (MCS) for edge-assisted computing. MCS3 addresses real-time challenges through a hardware–software co-design, introducing a MCS3-bridge for monitoring peripheral traffic with a dual-mode scheduler. MCS3 is implemented on the AMD Virtex VC709 FPGA and examined using comprehensive metrics. The experimental results show that MCS3 significantly improves the system-wide real-time performance with light overhead on both hardware and software.
Autonomous driving research has largely focused on safety while giving limited attention to non-functional aspects such as energy consumption and sustainability. As Autonomous Electric Vehicles (AEVs) become increasingly common in urban traffic, understanding how complex traffic dynamics influence their energy consumption is paramount to test whether AEVs can complete trips before battery depletion. To support energy-aware scenario-based testing of AEVs, we present E-CoDrive, a framework for reproducible closed-loop driving co-simulations that integrates an energy consumption model, a micro-traffic simulator, and a high-fidelity driving simulator to test AEV software stacks in urban scenarios. This tool paper describes the architecture of E-CoDrive and demonstrates its applicability by testing an Autoware-based AEV stack. Our evaluation shows that varying traffic conditions produce substantial differences in vehicle energy consumption. The artifact is publicly available at https://doi.org/10.6084/m9.figshare.32244783, and a screencast showing the tool is available at https://youtu.be/yX9fWHqCvgc.
Manfredi Napolitano, Alessandra Somma, Alessio Gambi et al.· 0 citations
This study addresses the challenges of communication delays and system stability in autonomous obstacle avoidance (AOA) systems under next-generation vehicular electronic/electrical architectures. A centralized PON-based architecture is proposed, leveraging XGSPON technology to enhance bandwidth capacity and reduce electromagnetic interference, while rigorously analyzing worst-case in-vehicle communication (IVOC) delays. To mitigate latency impacts, a Software-Defined Networking (SDN)-driven dynamic scheduling strategy prioritizes safety-critical data streams (e.g., environmental perception, motion control) through adaptive resource allocation. Further integrated with a robust H-infinity LQR controller, the co-design framework ensures precise trajectory tracking and suppresses steering oscillations under communication uncertainties. Simulation tests validate the framework's efficacy, demonstrating significant reductions in loop delays and improved dynamic stability in complex scenarios. This work bridges communication efficiency and control robustness, offering a scalable solution for advancing safety-critical autonomous driving systems.
SOVANET+ is presented, an extended scheduling technique that jointly accounts for service criticality, network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services, supporting its viability for next-generation intelligent transportation systems.
Athanasios Kanavos, Gerasimos Papanikolaou-Ntais, A. Kaloxylos· Electronics· 0 citations
Vehicle-to-everything (V2X)-enabled cooperative adaptive cruise control (CACC) is a key technology for improving both traffic efficiency and driving safety in vehicle-platooning scenarios. However, real-world platoons consist of heterogeneous vehicles with different actuation, computation, and mechanical delays, and communication latency also varies over time. Therefore, conventional approaches based on homogeneous vehicles and fixed-delay assumptions may fail to guarantee physical rear-end collision avoidance under severe driving conditions. This paper proposes Safe PF-CACC, a predictor-feedback-based CACC framework that integrates a V2X-aware safe inter-vehicle distance (Safe IV Distance) model with adaptive time-headway scheduling for heterogeneous vehicle platoons. The proposed Safe IV Distance is computed by considering communication latency, vehicle dynamic delays, and friction-dependent braking limits. It consists of three components: a minimum margin (MM) for low-speed and standstill conditions, a response-lag loss (RLL) induced by communication and vehicle dynamic delays, and a braking-performance limit (BPL) caused by road-friction-dependent braking capability. The resulting Safe IV Distance is converted into a dynamic effective time headway and incorporated into the predictor-feedback (PF) controller, while a filtering process is applied to suppress abrupt gain-scheduling variations. To evaluate the proposed framework, three representative CACC scenarios were considered: heterogeneous passenger-vehicle platooning, emergency vehicle platooning, and truck platooning. The simulation results show that overly short spacings without real-time delay awareness can cause collisions in high-speed and emergency driving scenarios, whereas overly conservative spacings improve safety at the cost of increased road occupancy. In the heterogeneous passenger-vehicle scenario, the proposed Safe PF-CACC reduces the maximum jerk and mean spacing by 20.6% and 49.4%, respectively, compared with the existing conservative method. In the emergency vehicle scenario, it achieved collision-free operation while reducing the maximum jerk and mean spacing by 18.6% and 53.2%, respectively. In the truck-platooning scenario, stable jerk and acceleration responses are maintained while the mean spacing is reduced by 59.6%. These results demonstrate that the proposed framework provides a practical integrated control approach for maintaining both control stability and physical safety in CACC systems under time-varying communication delays and road friction uncertainty.
Jaehyeon Shin, Junhyeok An, Sungjin Lee· Italian National Conference...· 0 citations
Modern ADAS systems often struggle with delayed hazard detection and localization on certain road conditions. These issues contribute towards an increase in traffic congestion and higher risk in driving environments. The challenges persist due to limited sensor reliability and also lack of comprehensive inter-vehicle awareness. This paper focuses on the 5G enabled vehicle-to-vehicle communication framework built over localized ADAS. This framework enhances cooperative driving using advanced sensor fusion and secure low latency messaging protocols. The system improves situational awareness and enables proactive collision avoidance by integrating UWB, GPS and machine learning based perception techniques. This advances smart city mobility by enabling cooperative awareness in dense urban traffic via 5G networks for intelligent transportation. Extensive simulation results validate this system's effectiveness and robustness across multiple safety critical driving scenarios.
Radhika M. Hirannaiah, Aritra Ghosh Dastidar, D. D et al.· International Conference on...· 0 citations
Vehicle-to-everything (V2X) communication plays a crucial role in enabling connected and autonomous driving by supporting the reliable exchange of safety-critical information among vehicles and infrastructure. However, due to the open nature of wireless channels, V2X systems are vulnerable to various physical-layer attacks, among which jamming is one of the most intuitive and severe threats. In this paper, we propose a vehicle speed-aware jammer-resilient reception framework for multiuser multiple-input multiple-output (MIMO) V2X systems. The proposed method exploits the fundamental difference in Doppler characteristics between stationary jammers and moving vehicles. By transforming the received signal into the Doppler domain, the receiver identifies low-Doppler components associated with static interference and suppresses them through Doppler-domain filtering. Notably, the proposed approach does not require prior knowledge of the jammer channel or its spatial direction, making it suitable for practical V2X environments. Simulation results demonstrate that the proposed framework effectively mitigates strong jamming signals and significantly improves the achievable sum-rate compared with conventional receivers.
Hanyoung Park, Yongjae Jang, Ji-Woong Choi· International Conference on...· 0 citations
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