Search Results (196)

Due to enclosed space, long evacuation distances, and complex path structures, key nodes in deep underground spaces are prone to forming bottlenecks during fire evacuation. To collect evacuation behavior data at potential bottlenecks, an interactive video-based hypothetical choice (HC) experiment was conducted with 104 valid samples. Exit distance, sub-safe zone setting, congestion, pedestrian flow guidance, and smoke were systematically examined. The results showed that: (a) exit distance, sub-safe zone setting, congestion at the nearest exit, and smoke significantly affected evacuation decisions, with clear avoidance of near-exit congestion and smoke; (b) congestion on paths to non-nearest exits had a relatively weak effect, and pedestrian flow guidance did not produce significant herding; and (c) gender, age, professional background, and evacuation experience influenced path choice differences under certain conditions. Notably, evacuees prioritized smoke avoidance over all other cues, while congestion triggered non-compensatory route switching rather than herding behavior. These findings enrich the empirical database on pedestrian evacuation dynamics in deep underground spaces and provide a quantitative basis for evacuation simulation, spatial optimization, and safety management. Full article

Pedestrian detection is a fundamental component of Advanced Driver Assistance Systems (ADAS) and plays a key role in collision avoidance and the safety of vulnerable road users. This paper presents a structured review of pedestrian detection methodologies developed between 2000 and 2025, spanning classical vision techniques and modern deep learning architectures. We organize the review into two phases. First, we examine classical methods, including Histogram of Oriented Gradients (HOG)+Support Vector Machine (SVM), Viola–Jones, Deformable Part Models, and Integral Channel Features, which established the conceptual foundations of the field. Then, we analyze state-of-the-art deep learning architectures, categorized by detector stage (one-stage vs. two-stage), localization strategy (anchor-based vs. anchor-free), feature extraction paradigm (Convolutional Neural Network (CNN)-based vs. transformer-based), output representation (bounding box vs. instance segmentation), and computational profile (lightweight vs. heavyweight). Several design principles introduced by classical methods remain visible in modern architectures, indicating that they were not fully superseded. The review also examines publicly available benchmark datasets and compares the strengths and limitations of camera-, Light Detection And Ranging (LiDAR)-, radar-, and multi-sensor-fusion-based systems for ADAS deployment. We close by identifying six open problems for the field: adversarial robustness, real-time inference under embedded constraints, detection under adverse weather, dataset bias and demographic fairness, the deployment of Bird’s-Eye View (BEV) and unified perception on automotive hardware, and explainability for safety-critical use. Full article

Road traffic accidents continue to be a major global cause of fatalities, disproportionately affecting pedestrians and other vulnerable road users. While deep reinforcement learning has proven effective in handling complex navigation tasks, providing formal stability and safety guarantees during both training and deployment remains a significant challenge. This paper introduces a dual-layer safety-aware framework for pedestrian avoidance in occluded urban driving. During training, a first-order Control Lyapunov–Barrier Function is integrated with Proximal Policy Optimization to promote goal-reaching stability and obstacle avoidance: the analytic Lie derivatives of the Lyapunov and barrier functions are embedded as a modifier in the advantage estimate, providing explicit stability and safety signals that accelerate convergence toward safe, goal-reaching behavior without disrupting the standard policy update. At deployment, a higher-order Control Lyapunov–Barrier Function, realized through a quadratic programming safety filter, acts as a safety shield that projects the nominal acceleration onto the intersection of the second-order Lyapunov and barrier feasibility sets; the barrier function is further extended with relative velocity terms to account for dynamic pedestrian motion. Experiments with a four-wheeled vehicle in the Webots simulator show that the framework reliably reaches the goal, avoids an occluded pedestrian across a range of crossing speeds, and improves task success rates and safety-constraint adherence relative to Proximal Policy Optimization and a conventional higher-order safety filter baseline, particularly during emergency braking maneuvers. Full article

Walkability has traditionally been assessed through physical indicators and objective metrics of the built environment; however, persistent methodological fragmentation limits its interpretive capacity in complex urban contexts. This article proposes an operational analytical framework for the analysis of walkability in Latin American intermediate commercial cities, understood as a relational and multiscale urban condition. The study adopts a qualitative–analytical design based on a systematic literature review and the comparative analysis of seven international walkability assessment methodologies. Through this critical synthesis, a framework is constructed that integrates macro, meso and micro scales, differentiated analytical domains, and a sequential interpretative procedure. The main contribution lies in providing an analytical structure that enables coherent interpretation of the tensions between urban structure, socio-economic functioning and pedestrian experience, avoiding reductive or decontextualized readings of walking in intermediate commercial cities. Full article

Electric vehicles introduce new considerations for pedestrian safety because their lower operating noise at low speeds may reduce pedestrian detectability in urban traffic environments. This study proposes a simulation-based integrated active–passive pedestrian protection framework for electric vehicles by linking automatic emergency braking, active hood deployment, and post-crash head injury assessment. A total of 688 valid pedestrian–vehicle crash records from the National Highway Traffic Safety Administration database were analyzed, and 5 representative pedestrian crash scenarios were constructed through clustering-informed scenario screening and a benchmark pedestrian AEB scenario. The scenarios were reconstructed in a PreScan–Simulink co-simulation environment to evaluate a time-to-collision-based AEB strategy, while the active hood system was assessed using multi-body dynamics simulation and finite element head impact analysis. The AEB results showed that three scenarios were avoided before pedestrian contact, whereas two remained unavoidable, with residual impact speeds of approximately 31.5 km/h and 46 km/h. The hood reached a stable deployed posture within approximately 0.1 s under the modeled conditions. The HIC₁₅ results at eight selected impact points showed that speed reduction and hood deployment generally reduced head injury metrics, but full compliance with the reference HIC₁₅ threshold of 1000 was not achieved at all points. These findings suggest that the proposed strategy can improve simulated pedestrian head protection performance under selected electric vehicle crash scenarios, while further structural optimization, experimental validation, and cost–benefit assessments are still required. Full article

Pedestrian trajectory prediction is vital for crowd analysis and human–-robot interaction. Recent deep models enhance accuracy by modeling social interactions and scene context, but they often remain opaque and rarely address privacy risks associated with learning individualized motion patterns. We propose CAMP, a Context-Aware, Multimodal, and Privacy-preserving pedestrian trajectory prediction framework designed around a role-aligned multimodal architecture, in which trajectory representations, dynamic scene cues, and explicit spatial interaction constraints are modeled through complementary branches. In CAMP, the trajectory encoder separates shared motion regularities from individualized motion tendencies, the optical-flow encoder captures motion-centric transient scene dynamics, and the potential-field encoder provides an interpretable spatial cost prior for obstacle avoidance and social interaction modeling. A Transformer-based decoder fuses these modalities to predict future trajectory distributions. To reduce the exposure of personalized motion patterns, we apply targeted DP-SGD only to the individual branch during the private fine-tuning stage, while treating the remaining frozen components as post-processing under the stated threat model. Experiments on the ETH/UCY benchmark show that CAMP achieves competitive ADE/FDE performance under the reported setting, while its private variant DP-CAMP maintains a reasonable utility–privacy trade-off across several reported privacy budgets. Full article

Pedestrian interaction remains a central safety challenge for autonomous driving, particularly under non-compliant or adversarial pedestrian behavior. Existing research and evaluations predominantly test against rule-following pedestrians, leaving a gap in understanding how learning-based agents handle worst-case interactions. We introduce the Jaywalkers Library, a novel configurable benchmark in CARLA with three adversarial pedestrian archetypes (Intruder, Indecisive Crosser, and Protester). We evaluate a DreamerV3 agent trained with sparse rewards, where the only pedestrian-specific signal is a terminal collision penalty. Evaluation employs a frozen-policy protocol with explicit train–test separation. Safety behavior is decomposed into endpoint outcomes, evasion dynamics, and efficiency costs. Under nominal conditions, the agent achieves high route completion and generalizes to an unseen town, whereas under adversarial exposure, an archetype-sensitive evasion strategy emerges. The agent swerves at speed against dynamic pedestrians but decelerates against the slow-moving Protester. Collision rates reveal a counterintuitive difficulty ordering in which the Protester is the hardest, followed by the Intruder, with the Indecisive Crosser as the most survivable. These findings show that a sparse terminal penalty suffices for emergent pedestrian avoidance in a world-model agent, but that effectiveness is bounded by the world model’s ability to predict pedestrian persistence. Full article

With the widespread application of autonomous vehicles (AVs), their dynamic interactions with other road users pose significant challenges to trajectory planning. Previous research on trajectory planning in shared spaces has mainly focused on generating smooth trajectories, while research considering the risks of human–vehicle interactions remains insufficient. Therefore, a risk-considered trajectory planning framework for autonomous vehicles is proposed. This framework includes two modules: pedestrian trajectory prediction and vehicle planning. In the prediction module, Social-STGCNN is used to predict pedestrian trajectories, obtaining a series of trajectories and probabilities, which serve as input to the planning module. To ensure the rationality of trajectory planning, a planning model is established in Frenet coordinates based on a quintic polynomial. Combining Bayesian and equality principles, a risk-considered cost function is designed. Under this framework, the risk value is calculated using the pedestrian trajectory prediction probability, and further Bayesian and equality costs are calculated. Based on the constraints, the trajectory with the minimum cost is solved. To evaluate the rationality of this framework, we designed simulation experiments for five typical high-conflict scenarios: overtaking in the same direction, head-on collision, pedestrian crossing, encountering pedestrians from multiple directions, and turning while encountering pedestrians crossing. Simultaneously, the framework is validated in a real-world environment. The results show that the proposed method can accurately capture pedestrians’ crossing intentions and effectively avoid pedestrians. The trajectory generated in the real environment is highly consistent with that of a driver, and it exhibits excellent adaptability and robustness in high-density mixed traffic environments. Full article

This paper presents ANAID (Autonomous Naturalistic obstacle-Avoidance Interaction Dataset), a new multimodal dataset designed to support research on autonomous driving, particularly with regard to obstacle avoidance and naturalistic driver–vehicle interaction. Data were collected using a Hyundai Tucson Hybrid equipped with a Comma-3X autonomous-driving development kit, combining high-resolution front-facing video with detailed CAN-bus telemetry. The dataset comprises four data collection campaigns, each corresponding to a single continuous driving session, yielding a total of 208 videos and 240,014 synchronized frames. In addition to the video data, the dataset provides vehicle state measurements (speed, acceleration, steering, pedal positions, turn signals, etc.) and an additional annotation layer identifying evasive maneuvers derived from steering-related signals. Data were recorded across four driving campaigns on an urban circuit at Universidad Europea de Madrid, capturing diverse real-world scenarios such as roundabouts, intersections, pedestrian areas, and segments requiring obstacle avoidance. A multi-stage processing pipeline aligns telemetry and visual data, extracts frames at 20 FPS, and detects evasive maneuvers using threshold-based time-series analysis. ANAID provides a fully aligned and non-destructive representation of naturalistic driving behavior, enabling research on control prediction, driver modeling, anomaly detection, and human–autonomy interaction in realistic traffic conditions. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article

As a typical biomimetic robotic system, quadruped robots replicate the flexible locomotion of quadruped mammals, outperforming wheeled robots in human-centered daily scenarios. To improve the social navigation adaptability of biomimetic quadruped robots in human–robot shared environments, this paper proposes a collision-aware orthogonal steering social force model (COSFM), an enhanced social force model that integrates collision prediction and social norms, inspired by human-like collision avoidance behaviors and social interaction rules. The model addresses key limitations of conventional social force models: delayed responses to dynamic pedestrians and inadequate consideration of pedestrians’ comfort zones. It introduces a time-to-collision prediction mechanism to mimic human predictive decision-making in dynamic social interactions, enhancing the robot’s anticipation of pedestrian motion intentions, and designs an orthogonal steering-based avoidance strategy for four typical human–robot interaction scenarios (head-on encounters, intersecting paths, active overtaking, passive yielding). This strategy replicates humans’ natural priority of lateral steering over abrupt deceleration or retreat, generating socially compliant trajectories aligned with human behavioral expectations. The proposed method is validated via simulation and real-world experiments on a Unitree Aliengo quadruped robot. Results show that the COSFM algorithm achieves a higher navigation success rate and better performance in path length, navigation time, and minimum human-robot distance than existing approaches, while its human-like lateral avoidance priority effectively preserves pedestrians’ psychological comfort zones, demonstrating robust social adaptability and great application potential for biomimetic legged robots. Full article

To address the limitations of conventional social force models in simulating high-density pedestrian crowds, this study proposes an enhanced model that incorporates visual perception constraints, group-type labeling, and collective avoidance mechanisms. Pedestrian trajectories were extracted from a bidirectional commercial street scenario using OpenCV, with YOLOv8 and DeepSORT employed for multiple object tracking. Analysis of pedestrian grouping patterns revealed that 52% of pedestrians walked in pairs, with distinct avoidance behaviors observed. The improved model integrates three key mechanisms: a restricted 120° forward visual field, group-type classification based on social relationships, and an exponentially formulated inter-group repulsive force. Simulation results in MATLAB R2023b demonstrate that the proposed model outperforms conventional approaches in multiple aspects: speed distribution (error < 8%); spatial density overlap (>85%); trajectory similarity (reduction of 32% in Dynamic Time Warping distance); and avoidance behavior accuracy (82% simulated vs. 85% measured). This model serves as a quantitative simulation tool and decision-making basis for the planning of pedestrian spaces, crowd organization management, and the optimization of emergency evacuation schemes in high-density pedestrian areas such as commercial streets and subway stations. Consequently, it contributes to enhancing pedestrian mobility efficiency and public safety, thereby supporting the development of a sustainable urban slow transportation system. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

The accurate simulative reconstruction of blind spot accidents requires innovative simulation methods. The objective of this paper is to analyze the avoidability of a specific blind spot accident and assess the impact of various parameters as if an active turning assistant had been installed in the truck. Additionally, it proposes a novel adaptation of the turning assistant system, along with an adapted simulation model tailored for drawbar trailers. The analyses presented in this paper were performed using PC-Crash accident simulation software, applying the “Active Safety” module. After performing a simulation of an accident involving a right-turning truck with a center axle trailer and a pedestrian, the avoidability of the accident was examined by simulating the scenario as if the truck involved in the accident had been equipped with an active turning assistant system. Subsequently, a parameter analysis was conducted to analyze the effect of changes in the active turning assistant’s parameters and changes in the pedestrian’s direction of entry on the avoidability of the accident. In doing so, we determined the parameters for the worst-case (collision) and the best-case (no collision) scenarios. Finally, an adaptation and further development of the active turning assistant, along with a corresponding simulation method for drawbar trailers, are proposed. Full article

Autonomous vehicles (AVs) are expected to operate safely and efficiently in complex urban environments characterized by dynamic and uncertain elements such as pedestrians, cyclists and adverse weather. Although current neural network-based decision-making algorithms, fuzzy logic and reinforcement learning have shown promise, they often struggle to handle ambiguous situations, such as partially hidden road signs or unpredictable human behavior. This paper proposes a new hybrid decision-making framework combining multi-agent reinforcement learning (MARL) and explainable artificial intelligence (XAI) to improve robustness, adaptability and transparency. Each agent of the MARL architecture is specialized in a specific sub-task (e.g., obstacle avoidance, trajectory planning, intention prediction), enabling modular and cooperative learning. XAI techniques are integrated to provide interpretable rationales for decisions, facilitating human understanding and regulatory compliance. The proposed system will be validated using CARLA simulator, combined with reference data, to demonstrate improved performance in safety-critical and ambiguous driving scenarios. Full article