A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
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 …
Highway decision-making and motion planning for autonomous driving via soft actor-critic
In this study, a decision-making and motion planning controller with continuous action space
is constructed in the highway driving scenario based on deep reinforcement learning. In the …
is constructed in the highway driving scenario based on deep reinforcement learning. In 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 …
Emsin: enhanced multi-stream interaction network for vehicle trajectory prediction
Predicting the future trajectories of dynamic traffic actors is the Gordian knot for autonomous
vehicles to achieve collision-free driving. Most existing works suffer from a gap in …
vehicles to achieve collision-free driving. Most existing works suffer from a gap in …
Vehicle actuator fault detection with finite-frequency specifications via Takagi-Sugeno fuzzy observers: Theory and experiments
This article presents a new nonlinear observer-based method to detect the faults of both
steering and torque actuators of autonomous ground vehicles. To this end, the nonlinear …
steering and torque actuators of autonomous ground vehicles. To this end, the nonlinear …
: Framework for Online Motion Planning Using Interaction-Aware Motion Predictions in Complex Driving Situations
Motion planning is a process of constant negotiation with the rest of the traffic agents and is
highly conditioned by their movement prediction. Indeed, an incorrect prediction could cause …
highly conditioned by their movement prediction. Indeed, an incorrect prediction could cause …
Legal Decision-making for Highway Automated Driving
X Ma, W Yu, C Zhao, C Wang, W Zhou… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Compliance with traffic laws is a fundamental requirement for human drivers on the road,
and autonomous vehicles must adhere to traffic laws as well. However, current autonomous …
and autonomous vehicles must adhere to traffic laws as well. However, current autonomous …
A Superposition Assessment Framework of Multi-Source Traffic Risks for Mega-Events Using Risk Field Model and Time-Series Generative Adversarial Networks
In this study, a novel traffic risk assessment framework of mega-events that integrate risk
field and deep learning is proposed. Considering the inherent difference of different traffic …
field and deep learning is proposed. Considering the inherent difference of different traffic …
A behavior decision method based on reinforcement learning for autonomous driving
Autonomous driving vehicles can reduce congestion and improve safety while increasing
traffic efficiency. To reflect the quality of driving more comprehensively, the driving safety …
traffic efficiency. To reflect the quality of driving more comprehensively, the driving safety …