GOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation

• URL: http://arxiv.org/abs/2406.03870v1
• Date: Thu, 6 Jun 2024 08:59:08 GMT
• Title: GOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation
• Authors: Joshua Ransiek, Johannes Plaum, Jacob Langner, Eric Sax,
• Abstract summary: Goal-conditioned Scenario Generation (GOOSE) is a goal-conditioned reinforcement learning (RL) approach that automatically generates safety-critical scenarios. We demonstrate the effectiveness of GOOSE in generating scenarios that lead to safety-critical events.
• Score: 0.14999444543328289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scenario-based testing is considered state-of-the-art for verifying and validating Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). However, the practical application of scenario-based testing requires an efficient method to generate or collect the scenarios that are needed for the safety assessment. In this paper, we propose Goal-conditioned Scenario Generation (GOOSE), a goal-conditioned reinforcement learning (RL) approach that automatically generates safety-critical scenarios to challenge ADASs or ADSs. In order to simultaneously set up and optimize scenarios, we propose to control vehicle trajectories at the scenario level. Each step in the RL framework corresponds to a scenario simulation. We use Non-Uniform Rational B-Splines (NURBS) for trajectory modeling. To guide the goal-conditioned agent, we formulate test-specific, constraint-based goals inspired by the OpenScenario Domain Specific Language(DSL). Through experiments conducted on multiple pre-crash scenarios derived from UN Regulation No. 157 for Active Lane Keeping Systems (ALKS), we demonstrate the effectiveness of GOOSE in generating scenarios that lead to safety-critical events.

Related papers

• 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)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems [0.0]
  This paper describes the framework for system-level simulation-based V&V of autonomous systems using AI components. The framework is based on a simulation model of the system, an abstract model that describes symbolically the system behavior. Various coverage criteria can be defined to guide the automated generation of the scenarios.
  arXiv  Detail & Related papers  (2023-11-16T11:03:13Z)
• 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)
• Is Scenario Generation Ready for SOTIF? A Systematic Literature Review [3.1491385041570146]
  We perform a Systematic Literature Review to identify techniques that generate scenarios complying with requirements of the SOTIF-standard. We investigate which details of the real-world are covered by generated scenarios, whether scenarios are specific for a system under test or generic, and whether scenarios are designed to minimize the set of unknown and hazardous scenarios.
  arXiv  Detail & Related papers  (2023-08-04T11:59:21Z)
• Clustering-based Criticality Analysis for Testing of Automated Driving Systems [0.18416014644193066]
  This paper focuses on the the goal to reduce the scenario set by clustering concrete scenarios from a single logical scenario. By employing clustering techniques, redundant and uninteresting scenarios can be identified and eliminated, resulting in a representative scenario set.
  arXiv  Detail & Related papers  (2023-06-22T08:36:20Z)
• Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior [135.78858513845233]
  STRIVE is a method to automatically generate challenging scenarios that cause a given planner to produce undesirable behavior, like collisions. To maintain scenario plausibility, the key idea is to leverage a learned model of traffic motion in the form of a graph-based conditional VAE. A subsequent optimization is used to find a "solution" to the scenario, ensuring it is useful to improve the given planner.
  arXiv  Detail & Related papers  (2021-12-09T18:03:27Z)
• Addressing the IEEE AV Test Challenge with Scenic and VerifAI [10.221093591444731]
  This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge. We demonstrate a systematic testing framework leveraging our previous work on formally-driven simulation for intelligent cyber-physical systems.
  arXiv  Detail & Related papers  (2021-08-20T04:51:27Z)
• 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)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)
• Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method [20.280573307366627]
  We propose a generative framework to create safety-critical scenarios for evaluating task algorithms. We demonstrate that the proposed framework generates safety-critical scenarios more efficiently than grid search or human design methods.
  arXiv  Detail & Related papers  (2020-03-02T21:26:03Z)

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.