Semantics for robotic mapping, perception and interaction: A survey
For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …
require a deeper understanding of the world in which they operate. In robotics and related …
Efficient deep reinforcement learning with imitative expert priors for autonomous driving
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …
driving. However, the low sample efficiency and difficulty of designing reward functions for …
Autonomous vehicle decision-making and control in complex and unconventional scenarios—a review
The development of autonomous vehicles (AVs) is becoming increasingly important as the
need for reliable and safe transportation grows. However, in order to achieve level 5 …
need for reliable and safe transportation grows. However, in order to achieve level 5 …
Learning multimodal rewards from rankings
Learning from human feedback has shown to be a useful approach in acquiring robot
reward functions. However, expert feedback is often assumed to be drawn from an …
reward functions. However, expert feedback is often assumed to be drawn from an …
Compositional learning and verification of neural network controllers
Recent advances in deep learning have enabled data-driven controller design for
autonomous systems. However, verifying safety of such controllers, which are often hard-to …
autonomous systems. However, verifying safety of such controllers, which are often hard-to …
DQ-GAT: Towards safe and efficient autonomous driving with deep Q-learning and graph attention networks
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …
A safe hierarchical planning framework for complex driving scenarios based on reinforcement learning
Autonomous vehicles need to handle various traffic conditions and make safe and efficient
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
Safety-assured speculative planning with adaptive prediction
Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
DRL-GAT-SA: Deep reinforcement learning for autonomous driving planning based on graph attention networks and simplex architecture
Y Peng, G Tan, H Si, J Li - Journal of Systems Architecture, 2022 - Elsevier
Self-driving cars need to make decisions while sharing the road with human drivers whose
behavior is uncertain. However, the presence of uncertainty leads to a trade-off between two …
behavior is uncertain. However, the presence of uncertainty leads to a trade-off between two …
Prediction-based reachability for collision avoidance in autonomous driving
Safety is an important topic in autonomous driving since any collision may cause serious
injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal …
injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal …