About

Self-driving cars : the task of making a car that can drive itself without human guidance.

( Image credit: Learning a Driving Simulator )

Benchmarks

No evaluation results yet. Help compare methods by submit evaluation metrics.

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.

PEDESTRIAN DETECTION SELF-DRIVING CARS

Learning a Driving Simulator

3 Aug 2016commaai/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.

SELF-DRIVING CARS VIDEO PREDICTION

End to End Learning for Self-Driving Cars

25 Apr 2016marsauto/europilot

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.

LANE DETECTION SELF-DRIVING CARS

Generating Contrastive Explanations with Monotonic Attribute Functions

29 May 2019Trusted-AI/AIX360

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.

CLASSIFICATION SELF-DRIVING CARS

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

3 Mar 2021vita-epfl/openpifpaf

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.

KEYPOINT DETECTION MULTI-PERSON POSE ESTIMATION SELF-DRIVING CARS

Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion

30 Mar 2021TRI-ML/packnet-sfm

Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars.

DEPTH ESTIMATION 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.

MONOCULAR DEPTH ESTIMATION SELF-DRIVING CARS

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.

MOTION FORECASTING MULTI-FUTURE TRAJECTORY PREDICTION SELF-DRIVING CARS TRAJECTORY FORECASTING

MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System

24 Oct 2016urbste/MultiCol-SLAM

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).

SELF-DRIVING CARS SIMULTANEOUS LOCALIZATION AND MAPPING