Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic
Prior
- URL: http://arxiv.org/abs/2112.05077v1
- Date: Thu, 9 Dec 2021 18:03:27 GMT
- Title: Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic
Prior
- Authors: Davis Rempe, Jonah Philion, Leonidas J. Guibas, Sanja Fidler, Or
Litany
- Abstract summary: 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.
- Score: 135.78858513845233
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Evaluating and improving planning for autonomous vehicles requires scalable
generation of long-tail traffic scenarios. To be useful, these scenarios must
be realistic and challenging, but not impossible to drive through safely. In
this work, we introduce STRIVE, 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.
Scenario generation is formulated as an optimization in the latent space of
this traffic model, effected by perturbing an initial real-world scene to
produce trajectories that collide with a given planner. A subsequent
optimization is used to find a "solution" to the scenario, ensuring it is
useful to improve the given planner. Further analysis clusters generated
scenarios based on collision type. We attack two planners and show that STRIVE
successfully generates realistic, challenging scenarios in both cases. We
additionally "close the loop" and use these scenarios to optimize
hyperparameters of a rule-based planner.
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