Generating and Explaining Corner Cases Using Learnt Probabilistic Lane
Graphs
- URL: http://arxiv.org/abs/2308.13658v2
- Date: Wed, 13 Mar 2024 02:08:34 GMT
- Title: Generating and Explaining Corner Cases Using Learnt Probabilistic Lane
Graphs
- Authors: Enrik Maci, Rhys Howard, Lars Kunze
- Abstract summary: We introduce Probabilistic Lane Graphs (PLGs) to describe a finite set of lane positions and directions in which vehicles might travel.
The structure of PLGs is learnt directly from historic traffic data.
We use reinforcement learning techniques to modify this policy to generate realistic and explainable corner case scenarios.
- Score: 5.309950889075669
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Validating the safety of Autonomous Vehicles (AVs) operating in open-ended,
dynamic environments is challenging as vehicles will eventually encounter
safety-critical situations for which there is not representative training data.
By increasing the coverage of different road and traffic conditions and by
including corner cases in simulation-based scenario testing, the safety of AVs
can be improved. However, the creation of corner case scenarios including
multiple agents is non-trivial. Our approach allows engineers to generate
novel, realistic corner cases based on historic traffic data and to explain why
situations were safety-critical. In this paper, we introduce Probabilistic Lane
Graphs (PLGs) to describe a finite set of lane positions and directions in
which vehicles might travel. The structure of PLGs is learnt directly from
spatio-temporal traffic data. The graph model represents the actions of the
drivers in response to a given state in the form of a probabilistic policy. We
use reinforcement learning techniques to modify this policy and to generate
realistic and explainable corner case scenarios which can be used for assessing
the safety of AVs.
Related papers
- ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles [17.396416459648755]
ChatScene is a Large Language Model (LLM)-based agent that generates safety-critical scenarios for autonomous vehicles.
A key part of our agent is a comprehensive knowledge retrieval component, which efficiently translates specific textual descriptions into corresponding domain-specific code snippets.
arXiv Detail & Related papers (2024-05-22T23:21:15Z) - A novel framework for adaptive stress testing of autonomous vehicles in
highways [3.2112502548606825]
We propose a novel framework to explore corner cases that can result in safety concerns in a highway traffic scenario.
We develop a new reward function for DRL to guide the AST in identifying crash scenarios based on the collision probability estimate.
The proposed framework is further integrated with a new driving model enabling us to create more realistic traffic scenarios.
arXiv Detail & Related papers (2024-02-19T04:02:40Z) - 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) - 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) - CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs [6.131026007721575]
Corner case scenarios are an essential tool for testing and validating the safety of autonomous vehicles (AVs)
We introduce a novel approach based on Heterogeneous Graph Neural Networks (HGNNs) to transform regular driving scenarios into corner cases.
Our model successfully learned to produce corner cases from input scene graphs, achieving 89.9% prediction accuracy on our testing dataset.
arXiv Detail & Related papers (2023-09-18T14:59:11Z) - 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) - Learning Interactive Driving Policies via Data-driven Simulation [125.97811179463542]
Data-driven simulators promise high data-efficiency for driving policy learning.
Small underlying datasets often lack interesting and challenging edge cases for learning interactive driving.
We propose a simulation method that uses in-painted ado vehicles for learning robust driving policies.
arXiv Detail & Related papers (2021-11-23T20:14:02Z) - 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) - 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) - AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [76.46575807165729]
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.
arXiv Detail & Related papers (2021-01-16T23:23:12Z)
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.