Self-Driving Cars
168 papers with code • 0 benchmarks • 15 datasets
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
Benchmarks
These leaderboards are used to track progress in Self-Driving Cars
Libraries
Use these libraries to find Self-Driving Cars models and implementations
DDAD
Lost and Found
Shifts
Talk2Car
BDD-X
Argoverse 2 Motion Forecasting
SOD
HPD
OC
ELAS
Most implemented papers
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
HarshayuGirase/PECNet
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ECCV 2020
In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
henry1iu/tnt-trajectory-prediction
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CVPR 2020
Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e. g. pedestrians and vehicles) and road context information (e. g. lanes, traffic lights).
Speeding up Semantic Segmentation for Autonomous Driving
zh320/realtime-semantic-segmentation-pytorch
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We propose a novel deep network architecture for image segmentation that keeps the high accuracy while being efficient enough for embedded devices.
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
umautobots/driving-in-the-matrix
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Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics.
Safety Verification of Deep Neural Networks
Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations.
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).
A0C: Alpha Zero in Continuous Action Space
A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go.
PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots.
Leveraging Latent Features for Local Explanations
As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level.
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
marko-vasic/moet
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By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.
