9 citations · 15 across the 5 of their papers we have counts for
8 papers
DriveGPT: Scaling Autoregressive Behavior Models for Driving
Xin Huang, Eric M. Wolff, Paul Vernaza +13
We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent…
Explainable deep learning improves human mental models of self-driving cars
Eoin M. Kenny, Akshay Dharmavaram, Sang Uk Lee +6
Self-driving cars increasingly rely on deep neural networks to achieve human-like driving. The opacity of such black-box planners makes it challenging to accurately anticipate when…
Driving in Real Life with Inverse Reinforcement Learning
Tung Phan-Minh, Forbes Howington, Ting-Sheng Chu +10
In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a d…
Failure-Tolerant Contract-Based Design of an Automated Valet Parking System using a Directive-Response Architecture
Josefine Graebener, Tung Phan-Minh, Jiaqi Yan +2
Increased complexity in cyber-physical systems calls for modular system design methodologies that guarantee correct and reliable behavior, both in normal operations and in the pres…
Rules of the Road: Safety and Liveness Guarantees for Autonomous Vehicles
Karena X. Cai, Tung Phan-Minh, Soon-Jo Chung +1
The ability to guarantee safety and progress for all vehicles is vital to the success of the autonomous vehicle industry. We present a framework for designing autonomous vehicle be…
Counter-example Guided Learning of Bounds on Environment Behavior
Yuxiao Chen, Sumanth Dathathri, Tung Phan-Minh +1
There is a growing interest in building autonomous systems that interact with complex environments. The difficulty associated with obtaining an accurate model for such environments…
CoverNet: Multimodal Behavior Prediction using Trajectory Sets
Tung Phan-Minh, Elena Corina Grigore, Freddy A. Boulton +2
We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regre…
Towards Assume-Guarantee Profiles for Autonomous Vehicles
Tung Phan-Minh, Karena X. Cai, Richard M. Murray
Rules or specifications for autonomous vehicles are currently formulated on a case-by-case basis, and put together in a rather ad-hoc fashion. As a step towards eliminating this pr…