-
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
The motion planners used in self-driving vehicles need to generate traje...
read it
-
MP3: A Unified Model to Map, Perceive, Predict and Plan
High-definition maps (HD maps) are a key component of most modern self-d...
read it
-
The Importance of Prior Knowledge in Precise Multimodal Prediction
Roads have well defined geometries, topologies, and traffic rules. While...
read it
-
INFER: INtermediate representations for FuturE pRediction
In urban driving scenarios, forecasting future trajectories of surroundi...
read it
-
Closing the Planning-Learning Loop with Application to Autonomous Driving in a Crowd
Imagine an autonomous robot vehicle driving in dense, possibly unregulat...
read it
-
DeepGoal: Learning to Drive with driving intention from Human Control Demonstration
Recent research on automotive driving developed an efficient end-to-end ...
read it
-
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
Simulation has the potential to massively scale evaluation of self-drivi...
read it
End-to-end Interpretable Neural Motion Planner
In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.
READ FULL TEXT
Comments
There are no comments yet.