AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

• URL: http://arxiv.org/abs/2101.06549v4
• Date: Sun, 16 Apr 2023 20:22:41 GMT
• Title: AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
• Authors: Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, Raquel Urtasun
• Abstract summary: We propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system. By simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
• Score: 76.46575807165729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible autonomy failures, such as perception failures due to occlusion. In this paper, we propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system. Given an initial traffic scenario, AdvSim modifies the actors' trajectories in a physically plausible manner and updates the LiDAR sensor data to match the perturbed world. Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack. Our experiments show that our approach is general and can identify thousands of semantically meaningful safety-critical scenarios for a wide range of modern self-driving systems. Furthermore, we show that the robustness and safety of these systems can be further improved by training them with scenarios generated by AdvSim.

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)
• NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving [19.709153559084093]
  We present a versatile NeRF-based simulator for testing autonomous driving software systems. The simulator learns from sequences of real-world driving sensor data. We use our simulator to test the responses of AD models to safety-critical scenarios inspired by Euro NCAP.
  arXiv  Detail & Related papers  (2024-04-11T14:03:16Z)
• 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)
• UniSim: A Neural Closed-Loop Sensor Simulator [76.79818601389992]
  We present UniSim, a neural sensor simulator that takes a single recorded log captured by a sensor-equipped vehicle. UniSim builds neural feature grids to reconstruct both the static background and dynamic actors in the scene. We incorporate learnable priors for dynamic objects, and leverage a convolutional network to complete unseen regions.
  arXiv  Detail & Related papers  (2023-08-03T17:56:06Z)
• 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)
• 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)
• Towards Automated Safety Coverage and Testing for Autonomous Vehicles with Reinforcement Learning [0.3683202928838613]
  Validation puts the autonomous vehicle system to the test in scenarios or situations that the system would likely encounter in everyday driving. We propose using reinforcement learning (RL) to generate failure examples and unexpected traffic situations for the AV software implementation.
  arXiv  Detail & Related papers  (2020-05-22T19:00:38Z)

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.