Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles

• URL: http://arxiv.org/abs/2103.07403v1
• Date: Fri, 12 Mar 2021 17:00:23 GMT
• Title: Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
• Authors: Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio, Timothy Tsai, Alejandro Troccoli, Stephen W. Keckler, Siddharth Garg, Anima Anandkumar
• Abstract summary: 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.
• Score: 86.9067793493874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient mechanisms to both characterize and generate testing scenarios using a state-of-the-art driving simulator. For any scenario, our method generates a set of possible driving paths and identifies all the possible safe driving trajectories that can be taken starting at different times, to compute metrics that quantify the complexity of the scenario. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project, as well as adversarial scenarios generated in simulation. 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. We demonstrate a strong correlation between the proposed metrics and human intuition.

Related papers

• ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable [88.08120417169971]
  Machine learning based autonomous driving systems often face challenges with safety-critical scenarios that are rare in real-world data. This work explores generating safety-critical driving scenarios by modifying complex real-world regular scenarios through trajectory optimization. Our approach addresses unrealistic diverging trajectories and unavoidable collision scenarios that are not useful for training robust planner.
  arXiv  Detail & Related papers  (2024-09-12T08:26:33Z)
• Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors [2.773055342671194]
  We introduce a natural adversarial scenario generation solution using naturalistic human driving priors and reinforcement learning techniques. Our findings demonstrate that the proposed model can generate realistic safety-critical test scenarios covering both naturalness and adversariality.
  arXiv  Detail & Related papers  (2024-08-06T13:58:56Z)
• Querying Labeled Time Series Data with Scenario Programs [0.0]
  We propose a formal definition of what constitutes a match between a real-world labeled time series data item and a simulated scenario. We present a definition and algorithm for matching scalable beyond the autonomous vehicles domain.
  arXiv  Detail & Related papers  (2024-06-25T15:15:27Z)
• DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving [76.29141888408265]
  We propose a large-scale dataset containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset.
  arXiv  Detail & Related papers  (2023-04-03T17:37:00Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• 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)
• Querying Labelled Data with Scenario Programs for Sim-to-Real Validation [5.8720142291102135]
  A fundamental question remains: are AV failure scenarios identified in simulation meaningful in reality, i.e., are they reproducible on the real system? An approach to validate simulated failure scenarios is to identify instances of the scenario in a corpus of real data, and check if the failure persists on the real data. We propose a formal definition of what it means for a labelled data item to match an abstract scenario, encoded as a scenario program using the SCENIC probabilistic programming language.
  arXiv  Detail & Related papers  (2021-12-01T01:04:13Z)
• Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction [88.0416857308144]
  We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
  arXiv  Detail & Related papers  (2020-08-13T17:20:02Z)
• Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation [3.2456691142503256]
  We show that transfer learning on simulated data sets provide better generalization and collision avoidance. Our results illustrate that information from a model trained on simulated data can be inferred to a model trained on real-world data.
  arXiv  Detail & Related papers  (2020-07-23T17:27:00Z)
• 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.