RCP-RF: A Comprehensive Road-car-pedestrian Risk Management Framework based on Driving Risk Potential Field

• URL: http://arxiv.org/abs/2305.02493v1
• Date: Thu, 4 May 2023 01:54:37 GMT
• Title: RCP-RF: A Comprehensive Road-car-pedestrian Risk Management Framework based on Driving Risk Potential Field
• Authors: Shuhang Tan, Zhiling Wang and Yan Zhong
• Abstract summary: We propose a comprehensive driving risk management framework named RCP-RF based on potential field theory under Connected and Automated Vehicles (CAV) environment. Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework. Empirical studies validate the superiority of our proposed framework against state-of-the-art methods on real-world dataset NGSIM and real AV platform.
• Score: 1.625213292350038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed the proliferation of traffic accidents, which led wide researches on Automated Vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies. However, existing time-based frameworks can not handle complex traffic scenarios and ignore the motion tendency influence of each moving objects on the risk distribution, leading to performance degradation. To address this problem, we novelly propose a comprehensive driving risk management framework named RCP-RF based on potential field theory under Connected and Automated Vehicles (CAV) environment, where the pedestrian risk metric are combined into a unified road-vehicle driving risk management framework. Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework, which can improve the performance of the driving risk model. Moreover, it requires only O(N 2) of time complexity in the proposed method. Empirical studies validate the superiority of our proposed framework against state-of-the-art methods on real-world dataset NGSIM and real AV platform.

Related papers

• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a novel diffusion-based controllable closed-loop safety-critical simulation framework. We develop a novel approach to simulate safety-critical scenarios through an adversarial term in the denoising process. We validate our framework empirically using the NuScenes dataset, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning [12.014977175887767]
  This study develops an autonomous driving strategy based on risk anticipation, considering the weights of surrounding vehicles. A risk indicator integrating surrounding vehicles weights, based on the risk field theory, is proposed and incorporated into autonomous driving decisions. An indicator, potential collision energy in conflicts, is newly proposed to evaluate the performance of the developed AV driving strategy.
  arXiv  Detail & Related papers  (2023-12-27T06:03:34Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [51.244807332133696]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior. Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving. RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• 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)
• A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness [52.27309191283943]
  This paper presents a data-driven framework for assessing the risk of different AVs' behaviors. We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
  arXiv  Detail & Related papers  (2023-08-02T09:48:08Z)
• A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections [5.8366275205801985]
  This study proposes a probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections. The proposed framework loosens restrictions of constant speed by predicting trajectories using a Gaussian Process Regression. Real-world LiDAR data collected at an intersection was used to evaluate the performance of the proposed framework.
  arXiv  Detail & Related papers  (2022-07-28T15:08:41Z)
• Risk Measurement, Risk Entropy, and Autonomous Driving Risk Modeling [0.0]
  This article examines the emerging technical difficulties, new ideas, and methods of risk modeling under autonomous driving scenarios. It provides technical feasibility for realizing risk assessment and car insurance pricing under a computer simulation environment.
  arXiv  Detail & Related papers  (2021-09-15T11:00:18Z)
• 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)
• Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [104.04999499189402]
  Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment. We propose an uncertainty-aware planning method, called emphrobust imitative planning (RIP) Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. We introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.
  arXiv  Detail & Related papers  (2020-06-26T11:07:32Z)

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.