47 citations · 80 across the 6 of their papers we have counts for
6 papers
MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning
Guangli Chen, Dianzhao Li, Wenjian Zhong +2
Robust urban autonomous driving requires reliable 3D scene understanding and stable decision-making under dense interactions. However, existing end-to-end models lack interpretabil…
Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving
Dianzhao Li, Ostap Okhrin
Autonomous vehicles hold great promise for reducing traffic fatalities and improving transportation efficiency, yet their widespread adoption hinges on embedding credible and trans…
Autonomous Driving Small-Scale Cars: A Survey of Recent Development
Dianzhao Li, Paul Auerbach, Ostap Okhrin
While engaging with the unfolding revolution in autonomous driving, a challenge presents itself, how can we effectively raise awareness within society about this transformative tre…
Vision-based DRL Autonomous Driving Agent with Sim2Real Transfer
Dianzhao Li, Ostap Okhrin
To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamen…
A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving
Dianzhao Li, Ostap Okhrin
Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks across various research fields. However, transferring DRL agents to the real world is still…
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator
Dianzhao Li, Ostap Okhrin
In the autonomous driving field, fusion of human knowledge into Deep Reinforcement Learning (DRL) is often based on the human demonstration recorded in a simulated environment. Thi…