A collection of papers focus on self-driving car. Many topics are covered including system architecture,computer vison, sensor fusion,planning&control and SLAM. The paper list will be timely updated.
- Junior: The Stanford Entry in the Urban Challenge [pdf]
- Towards Fully Autonomous Driving: Systems and Algorithms [pdf]
- Autonomous Driving in Urban Environments: Boss and the Urban Challenge [pdf]
- A Perception-Driven Autonomous Urban Vehicle [pdf]
- Making Bertha Drive—An Autonomous Journey on a Historic Route [pdf]
- Towards Full Automated Drive in Urban Environments:A Demonstration in GoMentum Station, California [pdf]
- Computer Vision for Autonomous Vehicles:Problems, Datasets and State-of-the-Art [pdf]
- Video Scene Parsing with Predictive Feature Learning[pdf]
- Unsupervised Monocular Depth Estimation with Left-Right Consistency [pdf]
- Learning a Driving Simulator [pdf]
- Deep Tracking:Seeing Beyond Seeing Using Recurrent Neural Networks [pdf]
- End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks [pdf]
- Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks [pdf]
- Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image [pdf]
- 3D Object Proposals using Stereo Imagery for Accurate Object Class Detection [pdf]
- On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training [pdf]
- End to End Learning for Self-Driving Cars [pdf]
- Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car [pdf]
- DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving [pdf]
- End-to-end Learning of Driving Models from Large-scale Video Datasets [pdf]
- Fully Convolutional Networks for Semantic Segmentation [pdf]
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation[pdf]
- Feature Pyramid Networks for Object Detection[pdf]
- Mask R-CNN [pdf]
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[pdf]
- Fast R-CNN [pdf]
- You Only Look Once:Unified, Real-Time Object Detection [pdf]
- YOLO9000: Better,Faster, Stronger [pdf]
- SSD: Single Shot MultiBox Detector [pdf]
- R-FCN: Object Detection via Region-based Fully Convolutional Networks [pdf]
- Predicting Deeper into the Future of Semantic Segmentation [pdf]
- Geometry-Based Next Frame Prediction from Monocular Video [pdf]
- Long-term Recurrent Convolutional Networks for Visual Recognition and Description [pdf]
- MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving [pdf]
- Beyond Skip Connections: Top-Down Modulation for Object Detection [pdf]
- Traffic Sign Recognition with Multi-Scale Convolutional Networks [pdf]
- Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs? [pdf]
- Pyramid Scene Parsing Network [pdf]
- Brain Inspired Cognitive Model with Attention for Self-Driving Cars [pdf]
- Image-to-Image Translation with Conditional Adversarial Networks [pdf]
- Unsupervised Image-to-Image Translation Networks [pdf]
- A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection[pdf]
- Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers [pdf]
- Multi-Class Multi-Object Tracking using Changing Point Detection[pdf]
- Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection[pdf]
- Overview of Environment Perception for Intelligent Vehicles[pdf]
- An Empirical Evaluation of Deep Learning on Highway Driving [pdf]
- Histograms of Oriented Gradients for Human Detection [pdf]
- LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks [pdf]
- A vision-centered multi-sensor fusing approach to self-localization and obstacle perception for robotic cars [pdf]
- Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture [pdf]
- Multi-View 3D Object Detection Network for Autonomous Driving [pdf]
- VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem [pdf]
- Vehicle Detection from 3D Lidar Using Fully Convolutional Network[pdf]
- Detecting Drivable Area for Self-driving Cars:An Unsupervised Approach [pdf]
- A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles[pdf]
- A Review of Motion Planning Techniques for Automated Vehicles [pdf]
- Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions [pdf]
- A survey on motion prediction and risk assessment for intelligent vehicles [pdf]
- End-to-End Deep Reinforcement Learning for Lane Keeping Assist [pdf]
- Deep Reinforcement Learning framework for Autonomous Driving [pdf]
- Continuous control with deep reinforcement learning [pdf]
- Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks [pdf]
- Long-term Planning by Short-term Prediction [pdf]
- Safe,Multi-Agent, Reinforcement Learning for Autonomous Driving [pdf]
- Large-scale cost function learning for path planning using deep inverse reinforcement learning [pdf]
- Human-like Planning of Swerve Maneuvers for Autonomous Vehicles [pdf]
- Virtual to Real Reinforcement Learning for Autonomous Driving [pdf]
- Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation [pdf]
- A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning [pdf]
- Navigating Intersections with Autonomous Vehicles using Deep Reinforcement Learning [pdf]
- Characterizing Driving Styles with Deep Learning [pdf]
- Learning Where to Attend Like a Human Driver [pdf]
- Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network [pdf]
- Past, Present, and Future of Simultaneous ocalization and Mapping: Toward theRobust-PerceptionAge [pdf]
- Learning from Maps: Visual Common Sense for Autonomous Driving [pdf]
- A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles [pdf]
- Image-based localization using LSTMs for structured feature correlation [pdf]
- PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization[pdf]
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