DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Explore this paper's citation graph
- Type
- preprint
- Published
- 2015-05-01
- Cited by
- 1,872
- References
- 22
- Access
- Open access
- OpenAlex
- https://openalex.org/W2119112357
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15693605
Keywords
Affordance, Computer science, Convolutional neural network, Perception, Set (abstract data type)
References
- Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
- The Ecological Approach to Visual Perception
- Nonlinear Effects in the Dynamics of Car Following
- Against direct perception
- Scalable Object Detection Using Deep Neural Networks
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Understanding High-Level Semantics by Modeling Traffic Patterns
- DeepFlow: Large Displacement Optical Flow with Deep Matching
- Vision meets robotics: The KITTI dataset
- Real time detection of lane markers in urban streets
- Sparse scene flow segmentation for moving object detection in urban environments
- Neural Network Perception for Mobile Robot Guidance
- Off-Road Obstacle Avoidance through End-to-End Learning
- 3D Traffic Scene Understanding From Movable Platforms
- LIBSVM: A library for support vector machines
- Caffe: Convolutional Architecture for Fast Feature Embedding
- ImageNet classification with deep convolutional neural networks
- Object Detection with Discriminatively Trained Part Based Models
- Evolving large-scale neural networks for vision-based TORCS
- TORCS, The Open Racing Car Simulator
Cited by
- Resiliency of Deep Neural Networks under Quantization
- PLATO: Policy learning using adaptive trajectory optimization
- Look-Ahead Before You Leap: End-to-End Active Recognition by Forecasting the Effect of Motion
- Weakly Supervised Learning of Affordances
- VirtualWorlds as Proxy for Multi-object Tracking Analysis
- Model-driven Simulations for Deep Convolutional Neural Networks
- On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training
- Shallow Networks for High-accuracy Road Object-detection
- Convolutional Neural Networkを用いた魚画像認識
- Characterizing Driving Styles with Deep Learning
- Deep learning a grasp function for grasping under gripper pose uncertainty
- Watch this: Scalable cost-function learning for path planning in urban environments
- recognition by forecasting the effect of motion
- Visual autonomous road following by symbiotic online learning
- Adaptive learning based on guided exploration for decision making at roundabouts
- From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots
- Rain removal via shrinkage of sparse codes and learned rain dictionary
- Rain Removal via Shrinkage-Based Sparse Coding and Learned Rain Dictionary
- Extracting Cognition out of Images for the Purpose of Autonomous Driving
- VisualBackProp: visualizing CNNs for autonomous driving
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