Self-Driving Cars Edit Task
Computer Vision • Autonomous Vehicles
About
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
Benchmarks
Datasets
Greatest papers with code
Fast Algorithms for Convolutional Neural Networks
CVPR 2016
• XiaoMi/mace
• 
The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes.
Learning a Driving Simulator
• commaai/research
•
Comma. ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road.
End to End Learning for Self-Driving Cars
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
Generating Contrastive Explanations with Monotonic Attribute Functions
In this paper, we propose a method that can generate contrastive explanations for such data where we not only highlight aspects that are in themselves sufficient to justify the classification by the deep model, but also new aspects which if added will change the classification.
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
To minimize the number of cameras needed for surround perception, we utilize fisheye cameras.
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e. g., a person's body joints) in multiple frames.
Ranked #4 on
Multi-Person Pose Estimation
on COCO
KEYPOINT DETECTION MULTI-PERSON POSE ESTIMATION SELF-DRIVING CARS
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars.
3D Packing for Self-Supervised Monocular Depth Estimation
CVPR 2020
• TRI-ML/packnet-sfm
• 
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
CVPR 2018
• agrimgupta92/sgan
•
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
Ranked #10 on
Trajectory Prediction
on ETH/UCY
MOTION FORECASTING MULTI-FUTURE TRAJECTORY PREDICTION SELF-DRIVING CARS TRAJECTORY FORECASTING
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w. r. t the observed environment (scene).