Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …
vehicles plan future trajectories. We develop a partially observable Markov decision process …
Toward trustworthy decision-making for autonomous vehicles: A robust reinforcement learning approach with safety guarantees
While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
Collision avoidance/mitigation system: Motion planning of autonomous vehicle via predictive occupancy map
Despite development efforts toward autonomous vehicle technologies, the number of
collisions and driver interventions of autonomous vehicles tested in California seems to be …
collisions and driver interventions of autonomous vehicles tested in California seems to be …
A dynamic cooperative lane-changing model for connected and autonomous vehicles with possible accelerations of a preceding vehicle
The emerging connected and autonomous vehicle (CAV) technologies offer a promising
solution to design better lane-changing maneuvers that can reduce the negative impacts of …
solution to design better lane-changing maneuvers that can reduce the negative impacts of …
Towards robust decision-making for autonomous driving on highway
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
Trustworthy safety improvement for autonomous driving using reinforcement learning
Reinforcement learning (RL) can learn from past failures and has the potential to provide
self-improvement ability and higher-level intelligence. However, the current RL algorithms …
self-improvement ability and higher-level intelligence. However, the current RL algorithms …
Multiple vehicle cooperation and collision avoidance in automated vehicles: Survey and an AI-enabled conceptual framework
Prospective customers are becoming more concerned about safety and comfort as the
automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation …
automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation …
Trajgen: Generating realistic and diverse trajectories with reactive and feasible agent behaviors for autonomous driving
Q Zhang, Y Gao, Y Zhang, Y Guo… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can
be used for validation and verification of self-driving system performance without relying on …
be used for validation and verification of self-driving system performance without relying on …
Classifying travelers' driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning
Driving style can substantially impact mobility, safety, energy consumption, and vehicle
emissions. While a range of methods has been used in the past for driving style …
emissions. While a range of methods has been used in the past for driving style …
Intention‐Aware Autonomous Driving Decision‐Making in an Uncontrolled Intersection
W Song, G Xiong, H Chen - Mathematical Problems in …, 2016 - Wiley Online Library
Autonomous vehicles need to perform social accepted behaviors in complex urban
scenarios including human‐driven vehicles with uncertain intentions. This leads to many …
scenarios including human‐driven vehicles with uncertain intentions. This leads to many …