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Found 4 papers, 3 papers with code
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
We assess existing state-of-the-art planners on our benchmark and show that neither rule-based nor learning-based planners can safely navigate the interPlan scenarios.
Conditional Unscented Autoencoders for Trajectory Prediction
We leverage recent advances in the space of the VAE, the foundation of the CVAE, which show that a simple change in the sampling procedure can greatly benefit performance.
Rethinking Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review
We systematically review state-of-the-art deep learning-based prediction, planning, and integrated prediction and planning models.
Parting with Misconceptions about Learning-based Vehicle Motion Planning
The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting.
