SIREN-Bench: Behavior-Driven Generation and Evaluation of Emergency-Vehicle Interactions

• URL: http://arxiv.org/abs/2608.24094v1
• Date: Tue, 25 Aug 2026 05:41:34 GMT
• Title: SIREN-Bench: Behavior-Driven Generation and Evaluation of Emergency-Vehicle Interactions
• Abstract summary: We present textbfSIREN, a behavior-driven SUMO--CARLA co-language platform for generating EMVsimulation--civilian interactions.<n>We instantiate the platform as textbfSIREN-v1, comprising seven parameterized interaction templates across emergency levels L1--L3 and three behavior families.<n>We demonstrate the benchmark through three representative tasks: 3D object detection, trajectory prediction, and vision-language risk understanding.
• Score: 4.523530339704703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emergency vehicles (EMVs) can reorganize surrounding traffic as civilian vehicles brake, change lanes, or form rescue corridors in response to their passage. Evaluating these safety-critical interactions requires behavior-level control over both EMV privileges and civilian responses, together with consistent sensing and ground truth. Existing datasets and simulation benchmarks do not directly provide this combination. We present \textbf{SIREN}, a behavior-driven SUMO--CARLA co-simulation platform for generating EMV--civilian interactions. SIREN couples SUMO's network-level traffic evolution and behavior logic with CARLA's continuous vehicle control and synchronized onboard sensing; depending on the active behavior, the interaction is controlled by SUMO, CARLA, or jointly. We instantiate the platform as \textbf{SIREN-Bench-v1}, comprising seven parameterized interaction templates across emergency levels L1--L3 and three behavior families, with synchronized sensor observations and simulator-native annotations. We demonstrate the benchmark through three representative tasks: 3D object detection, trajectory prediction, and vision-language risk understanding. Evaluations of nine trajectory predictors, four LiDAR-based detectors, and five vision-language models reveal behavior-dependent failure modes. Traffic-clearance interactions are hardest for detection, privileged intersection traversal is hardest for prediction, and no learned predictor outperforms the constant-velocity reference on average. Vision-language models perform substantially better on normal traffic than on near-miss and collision events. These results demonstrate the value of behavior-centered benchmarking and establish SIREN as an extensible data-generation and evaluation platform for autonomous-driving and transportation safety research.

Related papers

• Knowledge-Data-Dual-Driven Reinforcement Learning for Autonomous Vehicle Control in Mixed Traffic [7.833666254318349]
  In mixed traffic, decision-making for autonomous vehicles (AVs) confronts three interrelated challenges.<n>First, physics-based priors fail to capture latent interactive vehicle intentions and diverse driver behaviors.<n>Second, abrupt maneuvers by surrounding vehicles cause non-stationarity, leaving long-tail safety events under-explored.<n>Third, hybrid action spaces destabilize unified RL training due to the different temporal scales of continuous car-following and discrete lane-changing maneuvers.
  arXiv  Detail & Related papers  (2026-08-14T02:07:13Z)
• End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation [59.188006170485046]
  E2E-CDiff is an end-to-end conditional diffusion framework for controllable and realistic scenario generation.<n>Differentiable guidance regulates speed, enforces drivable-area compliance, and supports collision-avoidance or collision-seeking behaviors.<n>E2E-CDiff also performs competitively as a learning-based ego planner.
  arXiv  Detail & Related papers  (2026-07-21T02:13:25Z)
• ReactSim-Bench: Benchmarking Reactive Behavior World Model Simulation in Autonomous Driving [56.96697737229895]
  Reactive capability is a key property of data-driven behavior world model simulators for autonomous driving simulation systems.<n>We introduce ReactSim-Bench for evaluating the reactive capability of behavior world model simulation in autonomous driving.
  arXiv  Detail & Related papers  (2026-06-12T03:11:06Z)
• Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout [55.19244743715421]
  Driver-WM is a driver-centric latent world model that rolls out in-cabin dynamics causally conditioned on out-cabin traffic context.<n>It unifies physical kinematics forecasting with auxiliary behavioral and emotional semantic recognition.<n>Driver-WM yields robust long-horizon geometric forecasting for reactive high-motion maneuvers.
  arXiv  Detail & Related papers  (2026-05-06T16:30:48Z)
• BATON: A Multimodal Benchmark for Bidirectional Automation Transition Observation in Naturalistic Driving [18.15118596168445]
  Existing driving automation (DA) systems rely on human drivers to decide when to engage DA.<n>BATON is a naturalistic dataset capturing real-world DA usage across 127 drivers, and 136.6 hours of driving.<n>The dataset synchronizes front-view video, in-cabin video, decoded CAN bus signals, radar-based lead-vehicle interaction, and GPS-derived route context.<n>Results show that front-view video captures road context but not driver state, while in-cabin video reflects driver readiness but not the external scene.
  arXiv  Detail & Related papers  (2026-04-08T16:29:24Z)
• HetroD: A High-Fidelity Drone Dataset and Benchmark for Autonomous Driving in Heterogeneous Traffic [49.31491001465465]
  HetroD is a dataset and benchmark for developing autonomous driving systems in heterogeneous environments.<n>HetroD targets the critical challenge of navi- gating real-world heterogeneous traffic dominated by vulner- able road users (VRUs)
  arXiv  Detail & Related papers  (2026-02-03T12:12:47Z)
• Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights [13.941873438238673]
  This paper presents a field dataset and behavioral analysis of Tesla's Traffic Light and Stop Sign Control (TLSSC)<n>We develop a taxonomy of TLSSC-TCD interaction behaviors (i.e., stopping, accelerating, and car following) and calibrate the Full Velocity Difference Model (FVDM)<n>Results reveal that stopping behavior is driven by strong responsiveness to both desired speed deviation and relative speed, whereas accelerating behavior is more conservative.
  arXiv  Detail & Related papers  (2025-10-31T05:38:32Z)
• BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios [5.193590097161461]
  We design a bi-level interaction decision-making algorithm (BIDA) that integrates interactive Monte Carlo tree search (MCTS) with deep reinforcement learning (DRL)<n>Specifically, we adopt three types of DRL algorithms to construct a reliable value network and policy network, which guide the online deduction process of interactive MCTS.<n> Experimental evaluations demonstrate that our BIDA not only enhances interactive deduction and reduces computational costs, but also outperforms other latest benchmarks.
  arXiv  Detail & Related papers  (2025-06-19T19:03:40Z)
• Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic [0.0]
  We aim to quantify consensus across safety, interaction quality, and traffic performance in automated vehicles (AVs)<n>Key metrics, including Time-to-Collision (TTC), Post-Encroachment Time (PET), deceleration patterns, headways, and string stability, are evaluated.<n>Results show that full consensus across safety, interaction, and performance is rare, with only 1.63% of AV-VRU interaction frames meeting all three conditions.
  arXiv  Detail & Related papers  (2025-05-07T12:59:59Z)
• PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario [1.1142444517901016]
  PRIMEDrive-CoT is a novel uncertainty-aware model for object interaction and Chain-of-Thought (CoT) reasoning in driving scenarios.<n>Our approach combines LiDAR-based 3D object detection with multi-view RGB references to ensure interpretable and reliable scene understanding.
  arXiv  Detail & Related papers  (2025-04-08T11:06:02Z)
• Reinforcement Learning with Latent State Inference for Autonomous On-ramp Merging under Observation Delay [6.0111084468944]
  We introduce the Lane-keeping, Lane-changing with Latent-state Inference and Safety Controller (L3IS) agent. L3IS is designed to perform the on-ramp merging task safely without comprehensive knowledge about surrounding vehicles' intents or driving styles. We present an augmentation of this agent called AL3IS that accounts for observation delays, allowing the agent to make more robust decisions in real-world environments.
  arXiv  Detail & Related papers  (2024-03-18T15:02:46Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.