Sample Efficient Interactive EndtoEnd Deep Learning for SelfDriving Cars with Selective MultiClass Safe Dataset Aggregation The objective of this paper is to develop a sample efficient endtoend deep learning method for selfdriving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driverxe2x80x99s policy. Endtoend imitation learning is a popular method for computing selfdriving car policies. The standard approach relies on collecting pairs of inputs (camera images) and outputs (steering angle, etc.) from an expert policy and fitting a deep neural network to this data to learn the driving policy. Although this approach had some successful demonstrations in the past, learning a good policy might require a lot of samples from the expert driver, which might be resourceconsuming. In this work, we develop a novel framework based on the Safe Dataset Aggregation (safe DAgger) approach, where the current learned policy is automatically segmented into different trajectory classes, and the algorithm identifies trajectory segmentsclasses with the weak performance at each step. Once the trajectory segments with weak performance identified, the sampling algorithm focuses on calling the expert policy only on these segments, which improves the convergence rate. The presented simulation results show that the proposed approach can yield significantly better performance compared to the standard Safe DAgger algorithm while using the same amount of samples from the expert.

Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.