Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic
- URL: http://arxiv.org/abs/2505.04379v1
- Date: Wed, 07 May 2025 12:59:59 GMT
- Title: Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic
- Authors: Mohammad Elayan, Wissam Kontar,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Transportation systems have long been shaped by complexity and heterogeneity, driven by the interdependency of agent actions and traffic outcomes. The deployment of automated vehicles (AVs) in such systems introduces a new challenge: achieving consensus across safety, interaction quality, and traffic performance. In this work, we position consensus as a fundamental property of the traffic system and aim to quantify it. We use high-resolution trajectory data from the Third Generation Simulation (TGSIM) dataset to empirically analyze AV and human-driven vehicle (HDV) behavior at a signalized urban intersection and around vulnerable road users (VRUs). Key metrics, including Time-to-Collision (TTC), Post-Encroachment Time (PET), deceleration patterns, headways, and string stability, are evaluated across the three performance dimensions. 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. These findings highlight the need for AV models that explicitly balance multi-dimensional performance in mixed-traffic environments. Full reproducibility is supported via our open-source codebase on https://github.com/wissamkontar/Consensus-AV-Analysis.
Related papers
- 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) - AI2-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamics [8.557684007368046]
This paper introduces an AI-enabled, interaction-aware active safety analysis framework.<n>The framework employs a bicycle model-augmented with road gradient considerations to accurately capture vehicle dynamics.<n>In parallel, a hypergraph-based AI model is developed to predict probabilistic trajectories of ambient traffic.
arXiv Detail & Related papers (2025-05-01T05:46:34Z) - Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework [62.47416496137193]
We propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop.<n>The architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime.
arXiv Detail & Related papers (2025-03-06T07:36:06Z) - Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices.
This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
arXiv Detail & Related papers (2024-11-04T09:21:00Z) - Automated Vehicles at Unsignalized Intersections: Safety and Efficiency Implications of Mixed-Human-Automated Traffic [6.9492069439607995]
The integration of automated vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency.<n>This study aims to bridge the gap by examining behavioral differences and adaptations of AVs and human-driven vehicles (HVs) at unsignalized intersections.<n>The findings reveal a paradox in mixed traffic flow: while AVs maintain larger safety margins, their conservative behavior can lead to unexpected situations for human drivers.
arXiv Detail & Related papers (2024-10-16T13:19:32Z) - CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions [13.981748780317329]
Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs)
This study introduces a novel accident anticipation framework for AVs, termed CRASH.
It seamlessly integrates five components: object detector, feature extractor, object-aware module, context-aware module, and multi-layer fusion.
Our model surpasses existing top baselines in critical evaluation metrics like Average Precision (AP) and mean Time-To-Accident (mTTA)
arXiv Detail & Related papers (2024-07-25T04:12:49Z) - 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) - Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories.
In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene.
We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
arXiv Detail & Related papers (2021-06-22T15:40:21Z) - Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention.
Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
arXiv Detail & Related papers (2021-04-19T11:48:13Z) - Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data
Augmentation [77.60050239225086]
We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images.
Our approach is fully automatic without any human interaction.
We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
arXiv Detail & Related papers (2020-12-15T03:03:38Z) - Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes.
We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way.
We show competitive detection and attribute recognition results, as well as a more stable MTL training.
arXiv Detail & Related papers (2020-12-04T15:10:12Z)
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.