Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations

• URL: http://arxiv.org/abs/2411.01909v1
• Date: Mon, 04 Nov 2024 09:21:00 GMT
• Title: Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations
• Authors: Michael Kurenkov, Sajad Marvi, Julian Schmidt, Christoph B. Rist, Alessandro Canevaro, Hang Yu, Julian Jordan, Georg Schildbach, Abhinav Valada,
• Abstract summary: 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.
• Score: 48.924085579865334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices and ensure seamless integration into human-dominated environments. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets, including Argoverse 2, nuPlan, Lyft, and DeepUrban. By defining and leveraging existing safety and behavior-related metrics, such as time to collision, adherence to speed limits, and interactions with other traffic participants, we aim to provide a comprehensive understanding of each datasets strengths and limitations. Our analysis focuses on the distribution of data samples, identifying noise, outliers, and undesirable behaviors exhibited by human drivers in both the training and validation sets. The results underscore the need for applying robust filtering techniques to certain datasets due to high levels of noise and the presence of such undesirable behaviors.

Related papers

• World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving [1.8277374107085946]
  We propose a comprehensive framework combining generative augmentation scene with adaptive temporal reasoning.<n>We develop a video generation pipeline that utilizes a world model by guided domain-informed prompts to create high-resolution, statistically consistent driving scenarios.<n>In parallel, we construct a dynamic prediction model that encodes-temporal relationships through strengthened graph convolutions and dilated temporal operators.
  arXiv  Detail & Related papers  (2025-07-17T03:34:54Z)
• Test Automation for Interactive Scenarios via Promptable Traffic Simulation [48.240394447516664]
  We introduce an automated method to generate realistic and safety-critical human behaviors for AV planner evaluation in interactive scenarios.<n>We parameterize complex human behaviors using low-dimensional goal positions, which are then fed into a promptable traffic simulator, ProSim.<n>To automate test generation, we introduce a prompt generation module that explores the goal domain and efficiently identifies safety-critical behaviors using Bayesian optimization.
  arXiv  Detail & Related papers  (2025-06-01T22:29:32Z)
• Towards Reliable and Interpretable Traffic Crash Pattern Prediction and Safety Interventions Using Customized Large Language Models [14.53510262691888]
  TrafficSafe is a framework that adapts to reframe crash prediction and feature attribution as text-level reasoning.<n>Alcohol-impaired driving is the leading factor in severe crashes.<n>TrafficSafe highlights pivotal features during model training guiding strategic crash data collection improvements.
  arXiv  Detail & Related papers  (2025-05-18T21:02:30Z)
• Offline Reinforcement Learning using Human-Aligned Reward Labeling for Autonomous Emergency Braking in Occluded Pedestrian Crossing [13.342097008372479]
  This paper presents a novel pipeline for generating human-aligned reward labels. The pipeline incorporates an adaptive safety component, activated by analyzing semantic segmentation maps. The results indicate that the generated reward labels closely match the simulation reward labels.
  arXiv  Detail & Related papers  (2025-04-11T17:11:21Z)
• Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights [18.92479778025183]
  In driving scenarios, a vehicle's trajectory is determined by the decision-making process of human drivers. Previous models fail to capture the true intentions of human drivers, leading to suboptimal performance in long-term trajectory prediction. We introduce a Cognitive-Informed Transformer (CITF) that incorporates a cognitive concept, Perceived Safety, to interpret drivers' decision-making mechanisms.
  arXiv  Detail & Related papers  (2025-02-27T13:43:17Z)
• D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation [6.890077875318333]
  Driver to Evaluation dataset (D2E) is an autonomous decision-making dataset. It contains data on driver states, vehicle states, environmental situations, and evaluation scores from human reviewers. D2E contains over 1100 segments of interactive driving case data covering from human driver factor to evaluation results.
  arXiv  Detail & Related papers  (2024-04-12T21:29:18Z)
• 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)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view. To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding [51.8579160500354]
  We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-03-17T00:28:33Z)
• Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms [2.064612766965483]
  We present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario. We analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics.
  arXiv  Detail & Related papers  (2022-08-10T19:59:16Z)
• Exploring the trade off between human driving imitation and safety for traffic simulation [0.34410212782758043]
  We show that a trade-off exists between imitating human driving and maintaining safety when learning driving policies. We propose a multi objective learning algorithm (MOPPO) that improves both objectives together.
  arXiv  Detail & Related papers  (2022-08-09T14:30:19Z)
• Learning Latent Traits for Simulated Cooperative Driving Tasks [10.009803620912777]
  We build a framework capable of capturing a compact latent representation of the human in terms of their behavior and preferences. We then build a lightweight simulation environment, HMIway-env, for modelling one form of distracted driving behavior. We finally use this environment to quantify both the ability to discriminate drivers and the effectiveness of intervention policies.
  arXiv  Detail & Related papers  (2022-07-20T02:27:18Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles [86.9067793493874]
  We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
  arXiv  Detail & Related papers  (2021-03-12T17:00:23Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars [1.160208922584163]
  We introduce a model-free, deep reinforcement learning approach to generate automated human-like driving policies. We study a static obstacle avoidance task on a two-lane highway road in simulation. We demonstrate that our approach leads to human-like driving policies.
  arXiv  Detail & Related papers  (2020-06-07T18:20:33Z)

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.