Search Results for author:
Found 4 papers, 2 papers with code
SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
We train our system directly from 1, 000 hours of driving logs and measure both realism, reactivity of the simulation as the two key properties of the simulation.
One Thousand and One Hours: Self-driving Motion Prediction Dataset
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.
What data do we need for training an AV motion planner?
If cheaper sensors could be used for collection instead, data availability would go up, which is crucial in a field where data volume requirements are large and availability is small.
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).
