Risk assessment is a crucial component of collision warning and avoidanc...
Developing autonomous vehicles (AVs) helps improve the road safety and
t...
Trajectory prediction is a fundamental problem and challenge for autonom...
Proper functioning of connected and automated vehicles (CAVs) is crucial...
Among different quantum algorithms, PQC for QML show promises on near-te...
In urban environments, the complex and uncertain intersection scenarios ...
Real-time safety systems are crucial components of intelligent vehicles....
Parameterized Quantum Circuits (PQC) are drawing increasing research int...
The deep reinforcement learning-based energy management strategies (EMS)...
Precisely modeling interactions and accurately predicting trajectories o...
An efficient and reliable multi-agent decision-making system is highly
d...
Quantum Neural Network (QNN) is a promising application towards quantum
...
Point cloud has been widely used in the field of autonomous driving sinc...
This paper proposes a life-long adaptive path tracking policy learning m...
Federated learning (FL) is a distributed machine learning paradigm that
...
Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (...
Autonomous vehicles have a great potential in the application of both ci...
Unmanned vehicles often need to locate targets with high precision durin...
Representation learning over graph structure data has been widely studie...
This paper develops and summarizes the work of building the autonomous
i...
The urban intersection is a typically dynamic and complex scenario for
i...
Unmanned ground vehicles have a huge development potential in both civil...
This paper introduces the autonomous system of the "Smart Shark II" whic...
Cross-lingual transfer, where a high-resource transfer language is used ...
Language documentation is inherently a time-intensive process; transcrip...
Considering the driving habits which are learned from the naturalistic
d...