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SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e. g. avoiding collision, assuring physical feasibility).
Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts
The code for training and testing our model on a public prediction dataset and the video of the road test are available at https://woven. mobi/safepathnet
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