[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
A review and comparative study on probabilistic object detection in autonomous driving
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …
recent years, deep learning has become the de-facto approach for object detection, and …
End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Probabilistic end-to-end vehicle navigation in complex dynamic environments with multimodal sensor fusion
All-day and all-weather navigation is a critical capability for autonomous driving, which
requires proper reaction to varied environmental conditions and complex agent behaviors …
requires proper reaction to varied environmental conditions and complex agent behaviors …
VTGNet: A vision-based trajectory generation network for autonomous vehicles in urban environments
Traditional methods for autonomous driving are implemented with many building blocks
from perception, planning and control, making them difficult to generalize to varied scenarios …
from perception, planning and control, making them difficult to generalize to varied scenarios …
Object-aware regularization for addressing causal confusion in imitation learning
Behavioral cloning has proven to be effective for learning sequential decision-making
policies from expert demonstrations. However, behavioral cloning often suffers from the …
policies from expert demonstrations. However, behavioral cloning often suffers from the …
Learning resilient behaviors for navigation under uncertainty
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for
autonomous agents automatically. However, the underlying neural network polices have not …
autonomous agents automatically. However, the underlying neural network polices have not …
Deep imitation learning for autonomous navigation in dynamic pedestrian environments
Navigation through dynamic pedestrian environments in a socially compliant manner is still
a challenging task for autonomous vehicles. Classical methods usually lead to unnatural …
a challenging task for autonomous vehicles. Classical methods usually lead to unnatural …
DiGNet: Learning scalable self-driving policies for generic traffic scenarios with graph neural networks
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in
new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain …
new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain …