45 citations · 77 across the 7 of their papers we have counts for
4 papers · 1 filter
Out-of-distribution Detection in Classifiers via Generation
Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad +3
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples. For their real-world deployments, detecting out-of-dis…
Design Space of Behaviour Planning for Autonomous Driving
Marko Ilievski, Sean Sedwards, Ashish Gaurav +6
We explore the complex design space of behaviour planning for autonomous driving. Design choices that successfully address one aspect of behaviour planning can critically constrain…
Analysis of Confident-Classifiers for Out-of-distribution Detection
Sachin Vernekar, Ashish Gaurav, Taylor Denouden +4
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution). Therefore, detecting out-of-distributio…
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Jaeyoung Lee, Aravind Balakrishnan, Ashish Gaurav +2
Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge. A number of authors have…