Search Results for author:
Found 7 papers, 4 papers with code
World Models for Autonomous Driving: An Initial Survey
In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process.
A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving
In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency.
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles.
GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models
In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge.
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in Images
Anomaly detection in images plays a significant role for many applications across all industries, such as disease diagnosis in healthcare or quality assurance in manufacturing.
A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization
Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy.
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
