Imaging system design is a complex, time-consuming, and largely manual process; LiDAR design, ubiquitous in mobile devices, autonomous vehicles, and aerial imaging platforms, adds further complexity through unique spatial and temporal sampling requirements. In this work, we propose a framework for automated, task-driven LiDAR system design under arbitrary constraints. To achieve this, we represent LiDAR configurations in a continuous six-dimensional design space and learn task-specific implicit densities in this space via flow-based generative modeling. We then synthesize new LiDAR systems by modeling sensors as parametric distributions in 6D space and fitting these distributions to our learned implicit density using expectation-maximization, enabling efficient, constraint-aware LiDAR system design. We validate our method on diverse tasks in 3D vision, enabling automated LiDAR system design across real-world-inspired applications in face scanning, robotic tracking, and object detection.
In the intelligent era, the interaction between humans and intelligent systems fundamentally involves collaboration with autonomous intelligent agents. Human-AI Collaboration (HAC) represents a novel type of human-machine relationship facilitated by autonomous intelligent machines equipped with AI technologies. In this paradigm, AI agents serve not only as auxiliary tools but also as active teammates, partnering with humans to accomplish tasks collaboratively. Human-centered AI (HCAI) emphasizes that humans play critical leadership roles in the collaboration. This human-led collaboration imparts new dimensions to the human-machine relationship, necessitating innovative research perspectives, paradigms, and agenda to address the unique challenges posed by HAC. This chapter delves into the essence of HAC from the human-centered perspective, outlining its core concepts and distinguishing features. It reviews the current research methodologies and research agenda within the HAC field from the HCAI perspective, highlighting advancements and ongoing studies. Furthermore, a framework for human-centered HAC (HCHAC) is proposed by integrating these reviews and analyses. A case study of HAC in the context of autonomous vehicles is provided, illustrating practical applications and the synergistic interactions between humans and AI agents. Finally, it identifies potential future research directions aimed at enhancing the effectiveness, reliability, and ethical integration of human-centered HAC systems in diverse domains.
Autonomous Vehicles (AVs) collect and pseudo-label terabytes of multi-modal data localized to HD maps during normal fleet testing. However, identifying interesting and safety-critical scenarios from uncurated driving logs remains a significant challenge. Traditional scenario mining techniques are error-prone and prohibitively time-consuming, often relying on hand-crafted structured queries. In this work, we revisit spatio-temporal scenario mining through the lens of recent vision-language models (VLMs) to detect whether a described scenario occurs in a driving log and, if so, precisely localize it in both time and space. To address this problem, we introduce RefAV, a large-scale dataset of 10,000 diverse natural language queries that describe complex multi-agent interactions relevant to motion planning derived from 1000 driving logs in the Argoverse 2 Sensor dataset. We evaluate several referential multi-object trackers and present an empirical analysis of our baselines. Notably, we find that naively repurposing off-the-shelf VLMs yields poor performance, suggesting that scenario mining presents unique challenges. Lastly, we discuss our recent CVPR 2025 competition and share insights from the community. Our code and dataset are available at https://github.com/CainanD/RefAV/ and https://argoverse.github.io/user-guide/tasks/scenario_mining.html
Interpretability of point cloud (PC) models becomes imperative given their deployment in safety-critical scenarios such as autonomous vehicles. We focus on attributing PC model outputs to interpretable critical concepts, defined as meaningful subsets of the input point cloud. To enable human-understandable diagnostics of model failures, an ideal critical subset should be *faithful* (preserving points that causally influence predictions) and *conceptually coherent* (forming semantically meaningful structures that align with human perception). We propose InfoCons, an explanation framework that applies information-theoretic principles to decompose the point cloud into 3D concepts, enabling the examination of their causal effect on model predictions with learnable priors. We evaluate InfoCons on synthetic datasets for classification, comparing it qualitatively and quantitatively with four baselines. We further demonstrate its scalability and flexibility on two real-world datasets and in two applications that utilize critical scores of PC.
Software engineers in various industrial domains are already using Large Language Models (LLMs) to accelerate the process of implementing parts of software systems. When considering its potential use for ADAS or AD systems in the automotive context, there is a need to systematically assess this new setup: LLMs entail a well-documented set of risks for safety-related systems' development due to their stochastic nature. To reduce the effort for code reviewers to evaluate LLM-generated code, we propose an evaluation pipeline to conduct sanity-checks on the generated code. We compare the performance of six state-of-the-art LLMs (CodeLlama, CodeGemma, DeepSeek-r1, DeepSeek-Coders, Mistral, and GPT-4) on four safety-related programming tasks. Additionally, we qualitatively analyse the most frequent faults generated by these LLMs, creating a failure-mode catalogue to support human reviewers. Finally, the limitations and capabilities of LLMs in code generation, and the use of the proposed pipeline in the existing process, are discussed.
Neuromorphic computing, inspired by the human brain's neural architecture, is revolutionizing artificial intelligence and edge computing with its low-power, adaptive, and event-driven designs. However, these unique characteristics introduce novel cybersecurity risks. This paper proposes Neuromorphic Mimicry Attacks (NMAs), a groundbreaking class of threats that exploit the probabilistic and non-deterministic nature of neuromorphic chips to execute covert intrusions. By mimicking legitimate neural activity through techniques such as synaptic weight tampering and sensory input poisoning, NMAs evade traditional intrusion detection systems, posing risks to applications such as autonomous vehicles, smart medical implants, and IoT networks. This research develops a theoretical framework for NMAs, evaluates their impact using a simulated neuromorphic chip dataset, and proposes countermeasures, including neural-specific anomaly detection and secure synaptic learning protocols. The findings underscore the critical need for tailored cybersecurity measures to protect brain-inspired computing, offering a pioneering exploration of this emerging threat landscape.
Autonomous vehicles are typical complex intelligent systems with artificial intelligence at their core. However, perception methods based on deep learning are extremely vulnerable to adversarial samples, resulting in safety accidents. How to generate effective adversarial examples in the physical world and evaluate object detection systems is a huge challenge. In this study, we propose a unified joint adversarial training framework for both 2D and 3D samples to address the challenges of intra-class diversity and environmental variations in real-world scenarios. Building upon this framework, we introduce an adversarial sample reality enhancement approach that incorporates non-rigid surface modeling and a realistic 3D matching mechanism. We compare with 5 advanced adversarial patches and evaluate their attack performance on 8 object detecotrs, including single-stage, two-stage, and transformer-based models. Extensive experiment results in digital and physical environments demonstrate that the adversarial textures generated by our method can effectively mislead the target detection model. Moreover, proposed method demonstrates excellent robustness and transferability under multi-angle attacks, varying lighting conditions, and different distance in the physical world. The demo video and code can be obtained at https://github.com/Huangyh98/AdvReal.git.
Autonomous driving is reshaping the way humans travel, with millimeter wave (mmWave) radar playing a crucial role in this transformation to enabe vehicle-to-everything (V2X). Although chirp is widely used in mmWave radar systems for its strong sensing capabilities, the lack of integrated communication functions in existing systems may limit further advancement of autonomous driving. In light of this, we first design ``dedicated chirps" tailored for sensing chirp signals in the environment, facilitating the identification of idle time-frequency resources. Based on these dedicated chirps, we propose a chirp-division multiple access (Chirp-DMA) scheme, enabling multiple pairs of mmWave radar transceivers to perform integrated sensing and communication (ISAC) without interference. Subsequently, we propose two chirp-based delay-Doppler domain modulation schemes that enable each pair of mmWave radar transceivers to simultaneously sense and communicate within their respective time-frequency resource blocks. The modulation schemes are based on different multiple-input multiple-output (MIMO) radar schemes: the time division multiplexing (TDM)-based scheme offers higher communication rates, while the Doppler division multiplexing (DDM)-based scheme is suitable for working in a lower signal-to-noise ratio range. We then validate the effectiveness of the proposed DDM-based scheme through simulations. Finally, we present some challenges and issues that need to be addressed to advance ISAC in V2X for better autonomous driving. Simulation codes are provided to reproduce the results in this paper: \href{https://github.com/LiZhuoRan0/2025-IEEE-Network-ChirpDelayDopplerModulationISAC}{https://github.com/LiZhuoRan0}.
Achieving full automation in self-driving vehicles remains a challenge, especially in dynamic urban environments where navigation requires real-time adaptability. Existing systems struggle to handle navigation plans when faced with unpredictable changes in road layouts, spontaneous detours, or missing map data, due to their heavy reliance on predefined cartographic information. In this work, we explore the use of Large Language Models to generate Answer Set Programming rules by translating informal navigation instructions into structured, logic-based reasoning. ASP provides non-monotonic reasoning, allowing autonomous vehicles to adapt to evolving scenarios without relying on predefined maps. We present an experimental evaluation in which LLMs generate ASP constraints that encode real-world urban driving logic into a formal knowledge representation. By automating the translation of informal navigation instructions into logical rules, our method improves adaptability and explainability in autonomous navigation. Results show that LLM-driven ASP rule generation supports semantic-based decision-making, offering an explainable framework for dynamic navigation planning that aligns closely with how humans communicate navigational intent.
The transition from 5G networks to 6G highlights a significant demand for machine learning (ML). Deep learning models, in particular, have seen wide application in mobile networking and communications to support advanced services in emerging wireless environments, such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. The rapid expansion of Internet-of-Things (IoT) devices, many with limited computational capabilities, has accelerated the development of tiny machine learning (TinyML) and resource-efficient ML approaches for cost-effective services. However, the deployment of large-scale machine learning (LargeML) solutions require major computing resources and complex management strategies to support extensive IoT services and ML-generated content applications. Consequently, the integration of TinyML and LargeML is projected as a promising approach for future seamless connectivity and efficient resource management. Although the integration of TinyML and LargeML shows abundant potential, several challenges persist, including performance optimization, practical deployment strategies, effective resource management, and security considerations. In this survey, we review and analyze the latest research aimed at enabling the integration of TinyML and LargeML models for the realization of smart services and applications in future 6G networks and beyond. The paper concludes by outlining critical challenges and identifying future research directions for the holistic integration of TinyML and LargeML in next-generation wireless networks.
Road safety is a critical challenge, particularly for cyclists, who are among the most vulnerable road users. This study aims to enhance road safety by proposing a novel benchmark for bicycle occlusion level classification using advanced computer vision techniques. Utilizing a parts-based detection model, images are annotated and processed through a custom image detection pipeline. A novel method of bicycle occlusion level is proposed to objectively quantify the visibility and occlusion level of bicycle semantic parts. The findings indicate that the model robustly quantifies the visibility and occlusion level of bicycles, a significant improvement over the subjective methods used by the current state of the art. Widespread use of the proposed methodology will facilitate the accurate performance reporting of cyclist detection algorithms for occluded cyclists, informing the development of more robust vulnerable road user detection methods for autonomous vehicles.
A comprehensive understanding of 3D scenes is essential for autonomous vehicles (AVs), and among various perception tasks, occupancy estimation plays a central role by providing a general representation of drivable and occupied space. However, most existing occupancy estimation methods rely on LiDAR or cameras, which perform poorly in degraded environments such as smoke, rain, snow, and fog. In this paper, we propose 4D-ROLLS, the first weakly supervised occupancy estimation method for 4D radar using the LiDAR point cloud as the supervisory signal. Specifically, we introduce a method for generating pseudo-LiDAR labels, including occupancy queries and LiDAR height maps, as multi-stage supervision to train the 4D radar occupancy estimation model. Then the model is aligned with the occupancy map produced by LiDAR, fine-tuning its accuracy in occupancy estimation. Extensive comparative experiments validate the exceptional performance of 4D-ROLLS. Its robustness in degraded environments and effectiveness in cross-dataset training are qualitatively demonstrated. The model is also seamlessly transferred to downstream tasks BEV segmentation and point cloud occupancy prediction, highlighting its potential for broader applications. The lightweight network enables 4D-ROLLS model to achieve fast inference speeds at about 30 Hz on a 4060 GPU. The code of 4D-ROLLS will be made available at https://github.com/CLASS-Lab/4D-ROLLS.
Connected and Autonomous Vehicles (CAVs) enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network (CAN) bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robust security solutions. In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time. This survey provides a comprehensive review of state-of-the-art research on learning-based in-vehicle IDSs, focusing on Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) approaches. Based on the reviewed studies, we critically examine existing IDS approaches, categorising them by the types of attacks they detect - known, unknown, and combined known-unknown attacks - while identifying their limitations. We also review the evaluation metrics used in research, emphasising the need to consider multiple criteria to meet the requirements of safety-critical systems. Additionally, we analyse FL-based IDSs and highlight their limitations. By doing so, this survey helps identify effective security measures, address existing limitations, and guide future research toward more resilient and adaptive protection mechanisms, ensuring the safety and reliability of CAVs.
Online scene perception and topology reasoning are critical for autonomous vehicles to understand their driving environments, particularly for mapless driving systems that endeavor to reduce reliance on costly High-Definition (HD) maps. However, recent advances in online scene understanding still face limitations, especially in long-range or occluded scenarios, due to the inherent constraints of onboard sensors. To address this challenge, we propose a Standard-Definition (SD) Map Enhanced scene Perception and Topology reasoning (SEPT) framework, which explores how to effectively incorporate the SD map as prior knowledge into existing perception and reasoning pipelines. Specifically, we introduce a novel hybrid feature fusion strategy that combines SD maps with Bird's-Eye-View (BEV) features, considering both rasterized and vectorized representations, while mitigating potential misalignment between SD maps and BEV feature spaces. Additionally, we leverage the SD map characteristics to design an auxiliary intersection-aware keypoint detection task, which further enhances the overall scene understanding performance. Experimental results on the large-scale OpenLane-V2 dataset demonstrate that by effectively integrating SD map priors, our framework significantly improves both scene perception and topology reasoning, outperforming existing methods by a substantial margin.
Vision-Language Models (VLMs) have shown remarkable potential in advancing autonomous driving by leveraging multi-modal fusion in order to enhance scene perception, reasoning, and decision-making. Despite their potential, existing models suffer from computational overhead and inefficient integration of multi-view sensor data that make them impractical for real-time deployment in safety-critical autonomous driving applications. To address these shortcomings, this paper is devoted to designing a lightweight VLM called TS-VLM, which incorporates a novel Text-Guided SoftSort Pooling (TGSSP) module. By resorting to semantics of the input queries, TGSSP ranks and fuses visual features from multiple views, enabling dynamic and query-aware multi-view aggregation without reliance on costly attention mechanisms. This design ensures the query-adaptive prioritization of semantically related views, which leads to improved contextual accuracy in multi-view reasoning for autonomous driving. Extensive evaluations on the DriveLM benchmark demonstrate that, on the one hand, TS-VLM outperforms state-of-the-art models with a BLEU-4 score of 56.82, METEOR of 41.91, ROUGE-L of 74.64, and CIDEr of 3.39. On the other hand, TS-VLM reduces computational cost by up to 90%, where the smallest version contains only 20.1 million parameters, making it more practical for real-time deployment in autonomous vehicles.
Road terrains play a crucial role in ensuring the driving safety of autonomous vehicles (AVs). However, existing sensors of AVs, including cameras and Lidars, are susceptible to variations in lighting and weather conditions, making it challenging to achieve real-time perception of road conditions. In this paper, we propose an illumination-aware multi-modal fusion network (IMF), which leverages both exteroceptive and proprioceptive perception and optimizes the fusion process based on illumination features. We introduce an illumination-perception sub-network to accurately estimate illumination features. Moreover, we design a multi-modal fusion network which is able to dynamically adjust weights of different modalities according to illumination features. We enhance the optimization process by pre-training of the illumination-perception sub-network and incorporating illumination loss as one of the training constraints. Extensive experiments demonstrate that the IMF shows a superior performance compared to state-of-the-art methods. The comparison results with single modality perception methods highlight the comprehensive advantages of multi-modal fusion in accurately perceiving road terrains under varying lighting conditions. Our dataset is available at: https://github.com/lindawang2016/IMF.
Ensuring the safety and robustness of autonomous driving systems necessitates a comprehensive evaluation in safety-critical scenarios. However, these safety-critical scenarios are rare and difficult to collect from real-world driving data, posing significant challenges to effectively assessing the performance of autonomous vehicles. Typical existing methods often suffer from limited controllability and lack user-friendliness, as extensive expert knowledge is essentially required. To address these challenges, we propose LD-Scene, a novel framework that integrates Large Language Models (LLMs) with Latent Diffusion Models (LDMs) for user-controllable adversarial scenario generation through natural language. Our approach comprises an LDM that captures realistic driving trajectory distributions and an LLM-based guidance module that translates user queries into adversarial loss functions, facilitating the generation of scenarios aligned with user queries. The guidance module integrates an LLM-based Chain-of-Thought (CoT) code generator and an LLM-based code debugger, enhancing the controllability and robustness in generating guidance functions. Extensive experiments conducted on the nuScenes dataset demonstrate that LD-Scene achieves state-of-the-art performance in generating realistic, diverse, and effective adversarial scenarios. Furthermore, our framework provides fine-grained control over adversarial behaviors, thereby facilitating more effective testing tailored to specific driving scenarios.
Autonomous vehicles need a complete map of their surroundings to plan and act. This has sparked research into the tasks of 3D occupancy prediction, 3D scene completion, and 3D panoptic scene completion, which predict a dense map of the ego vehicle's surroundings as a voxel grid. Scene completion extends occupancy prediction by predicting occluded regions of the voxel grid, and panoptic scene completion further extends this task by also distinguishing object instances within the same class; both aspects are crucial for path planning and decision-making. However, 3D panoptic scene completion is currently underexplored. This work introduces a novel framework for 3D panoptic scene completion that extends existing 3D semantic scene completion models. We propose an Object Module and Panoptic Module that can easily be integrated with 3D occupancy and scene completion methods presented in the literature. Our approach leverages the available annotations in occupancy benchmarks, allowing individual object shapes to be learned as a differentiable problem. The code is available at https://github.com/nicolamarinello/OffsetOcc .
In this paper, we provide a theoretical framework for the coordination of connected and automated vehicles (CAVs) at signal-free intersections, accounting for the unexpected presence of pedestrians. First, we introduce a general vehicle-to-infrastructure communication protocol and a low-level controller that determines the optimal unconstrained trajectories for CAVs, in terms of fuel efficiency and travel time, to cross the intersection without considering pedestrians. If such an unconstrained trajectory is unattainable, we introduce sufficient certificates for each CAV to cross the intersection while respecting the associated constraints. Next, we consider the case where an unexpected pedestrian enters the road. When the CAV's sensors detect a pedestrian, an emergency mode is activated, which imposes certificates related to an unsafe set in the pedestrian's proximity area. Simultaneously, a re-planning mechanism is implemented for all CAVs to accommodate the trajectories of vehicles operating in emergency mode. Finally, we validate the efficacy of our approach through simulations conducted in MATLAB and RoadRunner softwares, which facilitate the integration of sensor tools and the realization of real-time implementation.
Enhancing the robustness of object detection systems under adverse weather conditions is crucial for the advancement of autonomous driving technology. This study presents a novel approach leveraging the diffusion model Instruct Pix2Pix to develop prompting methodologies that generate realistic datasets with weather-based augmentations aiming to mitigate the impact of adverse weather on the perception capabilities of state-of-the-art object detection models, including Faster R-CNN and YOLOv10. Experiments were conducted in two environments, in the CARLA simulator where an initial evaluation of the proposed data augmentation was provided, and then on the real-world image data sets BDD100K and ACDC demonstrating the effectiveness of the approach in real environments. The key contributions of this work are twofold: (1) identifying and quantifying the performance gap in object detection models under challenging weather conditions, and (2) demonstrating how tailored data augmentation strategies can significantly enhance the robustness of these models. This research establishes a solid foundation for improving the reliability of perception systems in demanding environmental scenarios, and provides a pathway for future advancements in autonomous driving.