A Robust Real-Time Lane Detection Method with Fog-Enhanced Feature Fusion for Foggy Conditions

Lane detection is a critical component of Advanced Driver Assistance Systems (ADAS). Existing lane detection algorithms generally perform well under favorable weather conditions. However, their performance degrades significantly in adverse conditions, such as fog, which increases the risk of traffic accidents. This challenge is compounded by the lack of specialized datasets and methods designed for foggy environments. To address this, we introduce the FoggyLane dataset, captured in real-world foggy scenarios, and synthesize two additional datasets, FoggyCULane and FoggyTusimple, from existing popular lane detection datasets. Furthermore, we propose a robust Fog-Enhanced Network for lane detection, incorporating a Global Feature Fusion Module (GFFM) to capture global relationships in foggy images, a Kernel Feature Fusion Module (KFFM) to model the structural and positional relationships of lane instances, and a Low-level Edge Enhanced Module (LEEM) to address missing edge details in foggy conditions. Comprehensive experiments demonstrate that our method achieves state-of-the-art performance, with F1-scores of 95.04 on FoggyLane, 79.85 on FoggyCULane, and 96.95 on FoggyTusimple. Additionally, with TensorRT acceleration, the method reaches a processing speed of 38.4 FPS on the NVIDIA Jetson AGX Orin, confirming its real-time capabilities and robustness in foggy environments.

A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective

Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion planning (MoP) challenges in autonomous driving (AD). Despite rapid advancements in RL and AD, a systematic description and interpretation of the RL design process tailored to diverse driving tasks remains underdeveloped. This survey provides a comprehensive review of RL-based MoP for AD, focusing on lessons from task-specific perspectives. We first outline the fundamentals of RL methodologies, and then survey their applications in MoP, analyzing scenario-specific features and task requirements to shed light on their influence on RL design choices. Building on this analysis, we summarize key design experiences, extract insights from various driving task applications, and provide guidance for future implementations. Additionally, we examine the frontier challenges in RL-based MoP, review recent efforts to addresse these challenges, and propose strategies for overcoming unresolved issues.

Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge

Foundation models (FMs) such as GPT-4 exhibit exceptional generative capabilities across diverse downstream tasks through fine-tuning. Split Federated Learning (SFL) facilitates privacy-preserving FM fine-tuning on resource-constrained local devices by offloading partial FM computations to edge servers, enabling device-edge synergistic fine-tuning. Practical edge networks often host multiple SFL tenants to support diversified downstream tasks. However, existing research primarily focuses on single-tenant SFL scenarios, and lacks tailored incentive mechanisms for multi-tenant settings, which are essential to effectively coordinate self-interested local devices for participation in various downstream tasks, ensuring that each SFL tenant's distinct FM fine-tuning requirements (e.g., FM types, performance targets, and fine-tuning deadlines) are met. To address this gap, we propose a novel Price-Incentive Mechanism (PRINCE) that guides multiple SFL tenants to offer strategic price incentives, which solicit high-quality device participation for efficient FM fine-tuning. Specifically, we first develop a bias-resilient global SFL model aggregation scheme to eliminate model biases caused by independent device participation. We then derive a rigorous SFL convergence bound to evaluate the contributions of heterogeneous devices to FM performance improvements, guiding the incentive strategies of SFL tenants. Furthermore, we model inter-tenant device competition as a congestion game for Stackelberg equilibrium (SE) analysis, deriving each SFL tenant's optimal incentive strategy. Extensive simulations involving four representative SFL tenant types (ViT, BERT, Whisper, and LLaMA) across diverse data modalities (text, images, and audio) demonstrate that PRINCE accelerates FM fine-tuning by up to 3.07x compared to state-of-the-art approaches, while consistently meeting fine-tuning performance targets.

Dynamic Pricing for On-Demand DNN Inference in the Edge-AI Market

The convergence of edge computing and AI gives rise to Edge-AI, which enables the deployment of real-time AI applications and services at the network edge. One of the fundamental research issues in Edge-AI is edge inference acceleration, which aims to realize low-latency high-accuracy DNN inference services by leveraging the fine-grained offloading of partitioned inference tasks from end devices to edge servers. However, existing research has yet to adopt a practical Edge-AI market perspective, which would systematically explore the personalized inference needs of AI users (e.g., inference accuracy, latency, and task complexity), the revenue incentives for AI service providers that offer edge inference services, and multi-stakeholder governance within a market-oriented context. To bridge this gap, we propose an Auction-based Edge Inference Pricing Mechanism (AERIA) for revenue maximization to tackle the multi-dimensional optimization problem of DNN model partition, edge inference pricing, and resource allocation. We investigate the multi-exit device-edge synergistic inference scheme for on-demand DNN inference acceleration, and analyse the auction dynamics amongst the AI service providers, AI users and edge infrastructure provider. Owing to the strategic mechanism design via randomized consensus estimate and cost sharing techniques, the Edge-AI market attains several desirable properties, including competitiveness in revenue maximization, incentive compatibility, and envy-freeness, which are crucial to maintain the effectiveness, truthfulness, and fairness of our auction outcomes. The extensive simulation experiments based on four representative DNN inference workloads demonstrate that our AERIA mechanism significantly outperforms several state-of-the-art approaches in revenue maximization, demonstrating the efficacy of AERIA for on-demand DNN inference in the Edge-AI market.

SparseFocus: Learning-based One-shot Autofocus for Microscopy with Sparse Content

Autofocus is necessary for high-throughput and real-time scanning in microscopic imaging. Traditional methods rely on complex hardware or iterative hill-climbing algorithms. Recent learning-based approaches have demonstrated remarkable efficacy in a one-shot setting, avoiding hardware modifications or iterative mechanical lens adjustments. However, in this paper, we highlight a significant challenge that the richness of image content can significantly affect autofocus performance. When the image content is sparse, previous autofocus methods, whether traditional climbing-hill or learning-based, tend to fail. To tackle this, we propose a content-importance-based solution, named SparseFocus, featuring a novel two-stage pipeline. The first stage measures the importance of regions within the image, while the second stage calculates the defocus distance from selected important regions. To validate our approach and benefit the research community, we collect a large-scale dataset comprising millions of labelled defocused images, encompassing both dense, sparse and extremely sparse scenarios. Experimental results show that SparseFocus surpasses existing methods, effectively handling all levels of content sparsity. Moreover, we integrate SparseFocus into our Whole Slide Imaging (WSI) system that performs well in real-world applications. The code and dataset will be made available upon the publication of this paper.

Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models

Pre-trained Language Models (PLMs) have demonstrated their superiority and versatility in modern Natural Language Processing (NLP), effectively adapting to various downstream tasks through further fine-tuning. Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising solution to address privacy and efficiency challenges in distributed training for PLMs on mobile devices. However, our measurements reveal two key limitations of FedPEFT: heterogeneous data leads to significant performance degradation, and a fixed parameter configuration results in communication inefficiency. To overcome these limitations, we propose FedARA, a novel Federated Adaptive Rank Allocation for parameter-efficient fine-tuning of language models. Specifically, FedARA employs truncated singular value decomposition (SVD) adaptation to enhance flexibility and expressiveness, significantly mitigating the adverse effects of data heterogeneity. Subsequently, it utilizes dynamic rank allocation to progressively identify critical ranks, effectively improving communication efficiency. Lastly, it leverages rank-based module pruning to remove inactive modules, steadily reducing local training time and peak memory usage in each round. Extensive experiments show that FedARA consistently outperforms weak baselines by an average of 8.49\% and strong baselines by 6.95\% across various datasets under data heterogeneity while significantly improving communication efficiency by 2.40\(\times\). Moreover, experiments on AGX Orin, Orin Nano and Raspberry Pi 5 devices demonstrate substantial decreases in total training time and energy consumption by up to 48.90\% and 46.95\%, respectively.

AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing Graphs

Missing attribute issues are prevalent in the graph learning, leading to biased outcomes in Graph Neural Networks (GNNs). Existing methods that rely on feature propagation are prone to cold start problem, particularly when dealing with attribute resetting and low-degree nodes, which hinder effective propagation and convergence. To address these challenges, we propose AttriReBoost (ARB), a novel method that incorporates propagation-based method to mitigate cold start problems in attribute-missing graphs. ARB enhances global feature propagation by redefining initial boundary conditions and strategically integrating virtual edges, thereby improving node connectivity and ensuring more stable and efficient convergence. This method facilitates gradient-free attribute reconstruction with lower computational overhead. The proposed method is theoretically grounded, with its convergence rigorously established. Extensive experiments on several real-world benchmark datasets demonstrate the effectiveness of ARB, achieving an average accuracy improvement of 5.11% over state-of-the-art methods. Additionally, ARB exhibits remarkable computational efficiency, processing a large-scale graph with 2.49 million nodes in just 16 seconds on a single GPU. Our code is available at https://github.com/limengran98/ARB.

Anti-bullying Adaptive Cruise Control: A proactive right-of-way protection approach

The current Adaptive Cruise Control (ACC) systems are vulnerable to "road bully" such as cut-ins. This paper proposed an Anti-bullying Adaptive Cruise Control (AACC) approach with proactive right-of-way protection ability. It bears the following features: i) with the enhanced capability of preventing bullying from cut-ins; ii) optimal but not unsafe; iii) adaptive to various driving styles of cut-in vehicles; iv) with real-time field implementation capability. The proposed approach can identify other road users' driving styles online and conduct game-based motion planning for right-of-way protection. A detailed investigation of the simulation results shows that the proposed approach can prevent bullying from cut-ins and be adaptive to different cut-in vehicles' driving styles. The proposed approach is capable of enhancing travel efficiency by up to 29.55% under different cut-in gaps and can strengthen driving safety compared with the current ACC controller. The proposed approach is flexible and robust against traffic congestion levels. It can improve mobility by up to 11.93% and robustness by 8.74% in traffic flow. Furthermore, the proposed approach can support real-time field implementation by ensuring less than 50 milliseconds computation time.

Federated Continual Learning for Edge-AI: A Comprehensive Survey

Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework, which fuses knowledge from different clients while preserving data privacy and retaining knowledge from previous tasks as it learns new ones. By so doing, FCL aims to ensure stable and reliable performance of learning models in dynamic and distributed environments. In this survey, we thoroughly review the state-of-the-art research and present the first comprehensive survey of FCL for Edge-AI. We categorize FCL methods based on three task characteristics: federated class continual learning, federated domain continual learning, and federated task continual learning. For each category, an in-depth investigation and review of the representative methods are provided, covering background, challenges, problem formalisation, solutions, and limitations. Besides, existing real-world applications empowered by FCL are reviewed, indicating the current progress and potential of FCL in diverse application domains. Furthermore, we discuss and highlight several prospective research directions of FCL such as algorithm-hardware co-design for FCL and FCL with foundation models, which could provide insights into the future development and practical deployment of FCL in the era of Edge-AI.

Human-Machine Shared Control Approach for the Takeover of Cooperative Adaptive Cruise Control

Cooperative Adaptive Cruise Control (CACC) often requires human takeover for tasks such as exiting a freeway. Direct human takeover can pose significant risks, especially given the close-following strategy employed by CACC, which might cause drivers to feel unsafe and execute hard braking, potentially leading to collisions. This research aims to develop a CACC takeover controller that ensures a smooth transition from automated to human control. The proposed CACC takeover maneuver employs an indirect human-machine shared control approach, modeled as a Stackelberg competition where the machine acts as the leader and the human as the follower. The machine guides the human to respond in a manner that aligns with the machine's expectations, aiding in maintaining following stability. Additionally, the human reaction function is integrated into the machine's predictive control system, moving beyond a simple "prediction-planning" pipeline to enhance planning optimality. The controller has been verified to i) enable a smooth takeover maneuver of CACC; ii) ensure string stability within a specific Operational Design Domain (ODD) when human control authority is below 32.7%; iii) enhance both perceived and actual safety through machine interventions; and iv) reduce the impact on upstream traffic by up to 60%.