Multi-modal fusion transformer for end-to-end autonomous driving
How should representations from complementary sensors be integrated for autonomous
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …
Neat: Neural attention fields for end-to-end autonomous driving
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
End-to-end urban driving by imitating a reinforcement learning coach
End-to-end approaches to autonomous driving commonly rely on expert demonstrations.
Although humans are good drivers, they are not good coaches for end-to-end algorithms …
Although humans are good drivers, they are not good coaches for end-to-end algorithms …
Exploring imitation learning for autonomous driving with feedback synthesizer and differentiable rasterization
We present a learning-based planner that aims to robustly drive a vehicle by mimicking
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to …
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to …
Sam: Squeeze-and-mimic networks for conditional visual driving policy learning
We describe a policy learning approach to map visual inputs to driving controls conditioned
on turning command that leverages side tasks on semantics and object affordances via a …
on turning command that leverages side tasks on semantics and object affordances via a …
Imitation learning of hierarchical driving model: from continuous intention to continuous trajectory
One of the challenges to reduce the gap between the machine and the human level driving
is how to endow the system with the learning capacity to deal with the coupled complexity of …
is how to endow the system with the learning capacity to deal with the coupled complexity of …
TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving
S Das, H Patibandla, S Bhattacharya… - Proceedings of the …, 2021 - openaccess.thecvf.com
Training vision-based Autonomous driving models is a challenging problem with enormous
practical implications. One of the main challenges is the requirement of storage and …
practical implications. One of the main challenges is the requirement of storage and …
A versatile and efficient reinforcement learning framework for autonomous driving
Heated debates continue over the best autonomous driving framework. The classic modular
pipeline is widely adopted in the industry owing to its great interpretability and stability …
pipeline is widely adopted in the industry owing to its great interpretability and stability …
[PDF][PDF] Model: A modularized end-to-end reinforcement learning framework for autonomous driving
Heated debates continue over the best autonomous driving framework. The classic modular
pipeline is widely adopted in the industry owing to its great interpretability and stability …
pipeline is widely adopted in the industry owing to its great interpretability and stability …
Iterative imitation policy improvement for interactive autonomous driving
We propose an imitation learning system for autonomous driving in urban traffic with
interactions. We train a Behavioral Cloning~(BC) policy to imitate driving behavior collected …
interactions. We train a Behavioral Cloning~(BC) policy to imitate driving behavior collected …