Search Results for author:
Found 5 papers, 4 papers with code
ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events
In this paper, we propose a black-box testing framework ReMAV that uses offline trajectories first to analyze the existing behavior of autonomous vehicles and determine appropriate thresholds to find the probability of failure events.
Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment
A benchmarking framework for the comparison of deep reinforcement learning in a vision-based autonomous driving will open up the possibilities for training better autonomous car driving policies.
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies
Autonomous cars are well known for being vulnerable to adversarial attacks that can compromise the safety of the car and pose danger to other road users.
DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing
We experimentally show that deep neural networks, as a simple regression model, can be efficiently used for test case prioritization in continuous integration testing.
Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI
Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI).
