Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks

• URL: http://arxiv.org/abs/2505.02050v1
• Date: Sun, 04 May 2025 09:58:02 GMT
• Title: Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks
• Authors: Kranthi Kumar Talluri, Anders L. Madsen, Galia Weidl,
• Abstract summary: Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions.<n>We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models.<n>This paves the way for robust, scalable, and efficient safety validation in automated driving systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems.

Related papers

• Towards provable probabilistic safety for scalable embodied AI systems [79.31011047593492]
  Embodied AI systems are increasingly prevalent across various applications.<n> Ensuring their safety in complex operating environments remains a major challenge.<n>This Perspective offers a pathway toward safer, large-scale adoption of embodied AI systems in safety-critical applications.
  arXiv  Detail & Related papers  (2025-06-05T15:46:25Z)
• 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)
• Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving [0.0]
  We propose a novel longitudinal control and collision avoidance algorithm that integrates adaptive cruising with emergency braking.<n>In simulated high risk scenarios, the algorithm effectively prevents potential pile up collisions, even in situations involving heavy duty vehicles.<n>In typical highway scenarios where three vehicles decelerate, the proposed DRL approach achieves a 99% success rate far surpassing the standard Federal Highway Administration speed concepts guide.
  arXiv  Detail & Related papers  (2025-04-26T14:17:06Z)
• Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
  We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles.<n>The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance.<n>We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
  arXiv  Detail & Related papers  (2025-01-16T16:45:08Z)
• Dynamic High-Order Control Barrier Functions with Diffuser for Safety-Critical Trajectory Planning at Signal-Free Intersections [9.041849642602626]
  Planning safe and efficient trajectories through signal-free intersections presents significant challenges for autonomous vehicles.<n>This study proposes a safety-critical planning method that integrates Dynamic High-Order Control Barrier Functions (DHOCBF) with a diffusion-based model, called DSC-Diffuser.<n>To further ensure driving safety in dynamic environments, the proposed DHOCBF framework dynamically adjusts to account for the movements of surrounding vehicles.
  arXiv  Detail & Related papers  (2024-11-29T11:57:00Z)
• 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)
• CAT: Closed-loop Adversarial Training for Safe End-to-End Driving [54.60865656161679]
  Adversarial Training (CAT) is a framework for safe end-to-end driving in autonomous vehicles. Cat aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios. Cat can effectively generate adversarial scenarios countering the agent being trained.
  arXiv  Detail & Related papers  (2023-10-19T02:49:31Z)
• Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems [8.561553195784017]
  This paper evaluates the security of the deep neural network based ACC systems under runtime perception attacks.<n>We present a context-aware strategy for the selection of the most critical times for triggering the attacks.<n>We evaluate the effectiveness of the proposed attack using an actual vehicle, a publicly available driving dataset, and a realistic simulation platform.
  arXiv  Detail & Related papers  (2023-07-18T03:12:03Z)
• Meta-Learning Priors for Safe Bayesian Optimization [72.8349503901712]
  We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
  arXiv  Detail & Related papers  (2022-10-03T08:38:38Z)
• Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving [100.57791628642624]
  We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
  arXiv  Detail & Related papers  (2022-03-04T16:14:33Z)
• Analyzing vehicle pedestrian interactions combining data cube structure and predictive collision risk estimation model [5.73658856166614]
  This study introduces a new concept of a pedestrian safety system that combines the field and the centralized processes. The system can warn of upcoming risks immediately in the field and improve the safety of risk frequent areas by assessing the safety levels of roads without actual collisions.
  arXiv  Detail & Related papers  (2021-07-26T23:00:56Z)

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.