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Found 7 papers, 0 papers with code
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations.
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.
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.
Explicit Domain Adaptation with Loosely Coupled Samples
In this work we propose a transfer learning framework, core of which is learning an explicit mapping between domains.
Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models
Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction.
Attention-based Lane Change Prediction
Lane change prediction of surrounding vehicles is a key building block of path planning.
Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction
One of the greatest challenges towards fully autonomous cars is the understanding of complex and dynamic scenes.
