DSDNet: Deep Structured self-Driving Network
arXiv:2008.06041
Abstract
In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network. Towards this goal, we develop a deep structured energy based model which considers the interactions between actors and produces socially consistent multimodal future predictions. Furthermore, DSDNet explicitly exploits the predicted future distributions of actors to plan a safe maneuver by using a structured planning cost. Our sample-based formulation allows us to overcome the difficulty in probabilistic inference of continuous random variables. Experiments on a number of large-scale self driving datasets demonstrate that our model significantly outperforms the state-of-the-art.
ECCV 2020
References in corpus (15)
- End to End Learning for Self-Driving Cars
- Loopy Belief Propagation for Approximate Inference: An Empirical Study
- Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
- CARLA: An Open Urban Driving Simulator
- Maximum Entropy Deep Inverse Reinforcement Learning
- Fully Connected Deep Structured Networks
- IntentNet: Learning to Predict Intention from Raw Sensor Data
- Baidu Apollo EM Motion Planner
- Driving Policy Transfer via Modularity and Abstraction
- MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
- Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
- V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
- Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
- Deep Structured Prediction with Nonlinear Output Transformations
- PIXOR: Real-time 3D Object Detection from Point Clouds