Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Behavioral intention prediction in driving scenes: A survey
In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
Incentive learning-based energy management for hybrid energy storage system in electric vehicles
Deep reinforcement learning has emerged as a promising candidate for online optimal
energy management of multi-energy storage vehicles. However, how to ensure the …
energy management of multi-energy storage vehicles. However, how to ensure the …
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. To address this challenge, we …
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
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 …
surrounding vehicles is paramount for ensuring safety and operational efficiency …
Driving style-aware energy management for battery/supercapacitor electric vehicles using deep reinforcement learning
Driving style can significantly affect the energy consumption, battery lifespan, and driving
economy of electric vehicles. In this context, this paper proposes a novel driving style-aware …
economy of electric vehicles. In this context, this paper proposes a novel driving style-aware …
Human observation-inspired trajectory prediction for autonomous driving in mixed-autonomy traffic environments
In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a
formidable challenge, especially in mixed autonomy environments. Traditional approaches …
formidable challenge, especially in mixed autonomy environments. Traditional approaches …
LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
To ensure safe driving in dynamic environments, autonomous vehicles should possess the
capability to accurately predict the lane change intentions of surrounding vehicles in …
capability to accurately predict the lane change intentions of surrounding vehicles in …
Driver lane change intention prediction based on topological graph constructed by driver behaviors and traffic context for human-machine co-driving system
Driver lane change intention (DLCI) predicting has become an essential research for the
development of human–machine co-driving system. This work makes an attempt to predict …
development of human–machine co-driving system. This work makes an attempt to predict …
Dynamic Spatio-temporal Graph Neural Network for Surrounding-aware Trajectory Prediction of Autonomous Vehicles
H Sadid, C Antoniou - IEEE Transactions on Intelligent Vehicles, 2024 - ieeexplore.ieee.org
Trajectory prediction is a critical aspect of understanding and estimating the motion of
dynamic systems, including robotics and autonomous vehicles (AVs). For safe and efficient …
dynamic systems, including robotics and autonomous vehicles (AVs). For safe and efficient …