Interactive Pedestrian-Aware System (IPAS)

• IPAS is an integrated sensing and decision-support framework that enables real-time estimation of pedestrian states and safety advisories in urban settings.
• It leverages diverse hardware—including roadside devices, wearable systems, and vehicle-based platforms—combined with computer vision, radar processing, and intent inference algorithms.
• Practical implementations demonstrate high detection accuracy, low inference latency, and robust multi-modal communication to significantly improve pedestrian safety and user trust.

An Interactive Pedestrian-Aware System (IPAS) is defined as an integrated sensing, perception, and decision-support platform that enables real-time anticipation, interpretation, and communication of pedestrian-related states and intentions in complex traffic or urban environments. The principal goal of IPAS is to enhance pedestrian safety and mobility—either by supporting vulnerable road users directly (e.g., visually impaired individuals), equipping vehicles with advanced intent-prediction and avoidance logic, providing safety advisories to drivers, or supporting urban infrastructure with context-aware alerts and controls. IPAS solutions span a diverse range of hardware instantiations (from mobile devices and embedded roadside units to vehicle-integrated perception suites and simulation environments) and leverage a wide variety of algorithmic frameworks, including computer vision, radar signal processing, intent inference, formal online monitoring, multimodal HMI design, and interactive retrieval paradigms.

1. Foundational Principles and System Taxonomy

IPAS encompasses both infrastructure-based and mobile-on-device solutions. Key classes include:

• Stationary Roadside Devices: Deployments such as vision-based alerting units attached to poles at crossings, employing optical flow for traffic gap detection and issuing multimodal alerts (audio, visual, vibration, wireless broadcast) (Perry et al., 2016).
• Wearable/Mobile Smartphone Systems: Embedded sensors (GPS, IMU, radar, Wi-Fi-Direct) support pedestrian localization, distraction detection, peer-to-peer vehicle communications, real-time risk estimation, and direct alerts to users (Won et al., 2018, Ppallan et al., 2023).
• Vehicle-Based Platforms: Systems integrated into AVs or ADAS stacks for intent-driven decision-making, occlusion-aware risk assessment, advisory generation based on aggregated maps, and formal safety monitoring (Varga et al., 2023, Matthews et al., 2017, Greer et al., 2023, Koc et al., 2021, Du et al., 2019).
• Human-in-the-Loop Simulation and Data Platforms: Omnidirectional panoramic simulators incorporating agent-based pedestrian behavior and interactive annotation for 4D urban scene understanding (Ge et al., 1 Dec 2025).
• Text-Based Interactive Retrieval: Interactive, zero-shot pedestrian identification in large-scale, open-world scenes, supporting semantic, visual, and multi-turn interaction (Luo et al., 20 Sep 2025).

This breadth is unified by the need for (1) real-time state estimation of pedestrians and traffic, (2) actionable prediction or classification of safety-critical events, and (3) interactive feedback loops (either human- or vehicle-facing) that influence behavior or provide warnings.

2. Sensing Modalities and Embedded Architectures

IPAS architectures exhibit substantial diversity in hardware and sensor configuration:

• Vision-Based Sensing: CMOS cameras or smartphone-embedded optics generate video streams. Roadside systems apply dense or sparse optical flow to compute influx maps, enabling projection of vehicle approach into a succinct activity signal (Perry et al., 2016). In AV stacks, multi-sensor fusion merges visual, LiDAR, and radar data for precise bounding-box tracking and feature extraction (Varga et al., 2023).
• Radar and RF Sensing: IR-UWB radar offers noise-resilient, short-range, multipath-resistant detection, integrated directly into mobile devices for real-time obstacle classification. Channel impulse response (CIR) features are processed over coherent processing intervals, with ANNs used for fine-grained surface and motion labeling (Ppallan et al., 2023).
• Positioning, IMU, and Environmental Sensing: GPS, accelerometers, magnetometers, and gyroscopes are fused (often via Kalman filtering or HMMs) for robust sidewalk-level localization and heading estimation, essential for mobile user support and context gating (Won et al., 2018, Zhong et al., 2019).
• Peer-to-Peer and V2X Telemetry: Direct Wi-Fi-Direct or V2X (DSRC/C-V2X) allows ultra-low-latency communication of pedestrian risk state, device context, or AV intent, supporting both safety-critical intervention and multi-agent negotiation (Won et al., 2018, Motamedi et al., 28 Aug 2025).
• Infrastructure/Simulator Integration: Cloud APIs serve digital intersection geometry and phase plans to on-device clients. In simulation, engine-level perception modules track all agent poses and mesh visibility via render-aligned hooks (Ge et al., 1 Dec 2025).

3. Algorithmic Frameworks for Perception and Intent Inference

The core of an IPAS is its perception and intent inference pipeline:

• Low-Level Signal Extraction: Vision-based systems compress high-dimensional optical flow or CIR sequences into 1D activity or risk signals via projection or FFT/statistical means (Perry et al., 2016, Ppallan et al., 2023). Machine-learning-augmented systems then apply rule-based detectors or ANN classifiers for obstacle or phone-activity classification.
• Latent State and Event Inference:
  • Intention Detection: Deep neural networks estimate the crossing probability, $$i_\mathrm{ped}=P(\text{crossing}\mid x_\mathrm{obs})$$, from fused raw observations (position, velocity, head orientation, gait) (Varga et al., 2023). Bayesian goal inference and particle filtering propagate multi-hypothesis pedestrian trajectories under fixed global maps (Du et al., 2019).
  • Social Context and Behavioral Feedback: Extended theory-of-planned-behavior (TPB) models quantify latent constructs such as Attitude, Perceived Behavioral Control, Trust, and Social Information, which are then used to infer probabilistic crossing intention and drive subsequent system response (Motamedi et al., 28 Aug 2025).
  • Occlusion-Aware Forecasting: Lightweight sigmoid-based models parameterized by contextual cues produce direct probabilities for imminent pedestrian emergence from occluded regions, coupled to continuous risk scanning (Koc et al., 2021).
• Interactive Retrieval and Aggregative Mapping: In open-world retrieval, multi-view semantic graphs, contrastive decoding, and hierarchical scoring enable robust human-in-the-loop disambiguation of visual queries (Luo et al., 20 Sep 2025). Map-aggregation frameworks accumulate detection history to learn persistent pedestrian hotspots and drive advisory logic via spatial probabilistic models and ball-tree indexing (Greer et al., 2023).

4. Decision and Control: Warning, Communication, and Actuation

Upon state estimation or intent detection, the following pathways are central to IPAS:

• Decision Rules and Matched-Filter Detection: Vision-based roadside units implement sliding-window matched filtering on 1D activity signals, with likelihood-ratio or Neyman–Pearson hypothesis testing for timely alerting and false-alarm minimization (Perry et al., 2016).
• Thresholding and Risk Fusion: Downstream alert decisions incorporate hard thresholds or risk fusion (Bayesian, DNN, or rule-based) across multiple cues (e.g., obstacle presence, collision probability) (Won et al., 2018, Ppallan et al., 2023, Koc et al., 2021).
• User and Driver Interaction Modalities:
  • Pedestrian Alerts: Multi-modal outputs (audio, visual, vibration, wireless push) are synthesized, with specific code-wordings or beeping patterns for varying risk states (Perry et al., 2016, Zhong et al., 2019).
  • Driver/Vehicle Actuation: LQR-based, jerk-constrained longitudinal controllers enforce safe deceleration or execute yield maneuvers as dictated by the current IPAS safety envelope or intent estimation (Koc et al., 2021).
  • External HMI/eHMI: Vehicle front- and side-mounted displays, strobe patterns, and audio speakers broadcast explicit intent states (e.g., “Please cross”, “Stopping”, “Yielding”) to ensure pedestrian interpretability and trust (Matthews et al., 2017, Motamedi et al., 28 Aug 2025).
  • V2X Data Exchange: Periodic and event-driven messages communicate crossing events, yield intention, and risk state among connected vehicles, infrastructure, and pedestrian smart devices (Won et al., 2018, Motamedi et al., 28 Aug 2025).

5. Evaluation, Benchmarking, and Field Deployment

Rigorous validation of IPAS involves both laboratory and field-centric testing with high empirical coverage:

• Detection and Classification Accuracy: Achievable performance includes $P_D>0.98$ for vision-based vehicle detection, obstacle classification $>95\%$ F1 on radar-vision fusion, phone-viewing detection $>90\%$, and warning time error $<1.6$ s (Perry et al., 2016, Ppallan et al., 2023, Won et al., 2018).
• Latency and Energy Metrics: End-to-end worst-case inference latency (<30 ms) and onboard processing requirements compatible with real-time cheap mobile or embedded platforms (<2 W, <40% CPU) (Ppallan et al., 2023, Perry et al., 2016).
• Behavioral and Human-Centric Outcomes: Integration of intention-aware communication systems increases measured pedestrian trust by >140%, and field studies show marked reductions in interaction latency and deadlock rate when intent-aware communication is enabled (Matthews et al., 2017).
• Advisory System Precision-Recall Tradeoff: Map-aggregation frameworks manage a computed tradeoff between missed advisories (Recall) and false alarms (Precision), tunable via spatial and temporal sampling parameters (Greer et al., 2023).
• Simulation and Data-Driven Development: Large-scale open-world or panoramic simulation enables acceleration of system development and benchmarking under controlled but realistic variation in pedestrian density, diversity, and behavior (Ge et al., 1 Dec 2025, Luo et al., 20 Sep 2025).

6. Limitations, Design Extensions, and Forward Directions

Current IPAS instantiations face several limitations and present ongoing opportunities for improvement:

• Sensing Constraints: Nighttime performance may degrade due to headlight/lighting saturation (vision), short effective range (UWB-radar), or GPS/cellular noise in dense urban settings (Perry et al., 2016, Ppallan et al., 2023, Zhong et al., 2019). Sensor fusion and fallback modalities (thermal, IMU, curb detection) are open research areas.
• Behavioral Model Generality: Existing DNNs and Bayesian models may be insufficiently robust under rare or ambiguous behaviors; robustness to semantic drift and open-set intent shift remains an open challenge in interactive retrieval (Luo et al., 20 Sep 2025).
• Scalability and Integration: Human annotation dependence, communication interoperability, and sub-meter localization accuracy are identified as bottlenecks for cross-city deployments and dense urban operation. Proposed solutions include online learning of confidence priors, dynamic map denoising, and extension to collaborative cloud-based frameworks (Greer et al., 2023).
• Human-Centric Evaluation: Current evaluation axes (Rank-K, mAP, TPB-predicted trust) may not fully capture the spectrum of real-world, human-facing risk and satisfaction. Calls for new, human-centric benchmarks and trusted communication protocols are noted (Luo et al., 20 Sep 2025, Motamedi et al., 28 Aug 2025).

In summary, the IPAS paradigm represents a confluence of algorithms, sensing modalities, and interaction models tightly coupled to real-world pedestrian safety requirements. Realized architectures demonstrate that commodity hardware, modest computational resources, and modern signal-processing and learning methods can jointly deliver quantifiable, context-adaptive safety for vulnerable users at critical risk points in the urban mobility landscape.