Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning

Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although punishing RL agents for risky situations can help to learn safe policies, it may also lead to highly conservative behavior. In this paper, we propose a distributional RL framework in order to learn adaptive policies that can tune their level of conservativity at run-time based on the desired comfort and utility. Using a proactive safety verification approach, the proposed framework can guarantee that actions generated from RL are fail-safe according to the worst-case assumptions. Concurrently, the policy is encouraged to minimize safety interference and generate more comfortable behavior. We trained and evaluated the proposed approach and baseline policies using a high level simulator with a variety of randomized scenarios including several corner cases which rarely happen in reality but are very crucial. In light of our experiments, the behavior of policies learned using distributional RL can be adaptive at run-time and robust to the environment uncertainty. Quantitatively, the learned distributional RL agent drives in average 8 seconds faster than the normal DQN policy and requires 83\% less safety interference compared to the rule-based policy with slightly increasing the average crossing time. We also study sensitivity of the learned policy in environments with higher perception noise and show that our algorithm learns policies that can still drive reliable when the perception noise is two times higher than the training configuration for automated merging and crossing at occluded intersections

Improving Trust in Deep Neural Networks with Nearest Neighbors

Deep neural networks are used increasingly for perception and decision-making in UAVs. For example, they can be used to recognize objects from images and decide what actions the vehicle should take. While deep neural networks can perform very well at complex tasks, their decisions may be unintuitive to a human operator. When a human disagrees with a neural network prediction, due to the black box nature of deep neural networks, it can be unclear whether the system knows something the human does not or whether the system is malfunctioning. This uncertainty is problematic when it comes to ensuring safety. As a result, it is important to develop technologies for explaining neural network decisions for trust and safety. This paper explores a modification to the deep neural network classification layer to produce both a predicted label and an explanation to support its prediction. Specifically, at test time, we replace the final output layer of the neural network classifier by a k-nearest neighbor classifier. The nearest neighbor classifier produces 1) a predicted label through voting and 2) the nearest neighbors involved in the prediction, which represent the most similar examples from the training dataset. Because prediction and explanation are derived from the same underlying process, this approach guarantees that the explanations are always relevant to the predictions. We demonstrate the approach on a convolutional neural network for a UAV image classification task. We perform experiments using a forest trail image dataset and show empirically that the hybrid classifier can produce intuitive explanations without loss of predictive performance compared to the original neural network. We also show how the approach can be used to help identify potential issues in the network and training process

Towards Learning Feasible Hierarchical Decision-Making Policies in Urban Autonomous Driving

Modern learning-based algorithms, powered by advanced deep structured neural nets, have multifacetedly facilitated automated driving platforms, spanning from scene characterization and perception to low-level control and state estimation schemes. Nonetheless, urban autonomous driving is regarded as a challenging application for machine learning (ML) and artificial intelligence (AI) since the learnt driving policies must handle complex multi-agent driving scenarios with indeterministic intentions of road participants. In the case of unsignalized intersections, automating the decision-making process at these safety-critical environments entails comprehending numerous layers of abstractions associated with learning robust driving behaviors to allow the vehicle to drive safely and efficiently. Based on our in-depth investigation, we discern that an efficient, yet safe, decision-making scheme for navigating real-world unsignalized intersections does not exist yet. The state-of-the-art schemes lacked practicality to handle real-life complex scenarios as they utilize Low-fidelity vehicle dynamic models which makes them incapable of simulating the real dynamic motion in real-life driving applications. In addition, the conservative behavior of autonomous vehicles, which often overreact to threats which have low likelihood, degrades the overall driving quality and jeopardizes safety. Hence, enhancing driving behavior is essential to attain agile, yet safe, traversing maneuvers in such multi-agent environments. Therefore, the main goal of conducting this PhD research is to develop high-fidelity learning-based frameworks to enhance the autonomous decision-making process at these safety-critical environments. We focus this PhD dissertation on three correlated and complementary research challenges. In our first research challenge, we conduct an in-depth and comprehensive survey on the state-of-the-art learning-based decision-making schemes with the objective of identifying the main shortcomings and potential research avenues. Based on the research directions concluded, we propose, in Problem II and Problem III, novel learning-based frameworks with the objective of enhancing safety and efficiency at different decision-making levels. In Problem II, we develop a novel sensor-independent state estimation for a safety-critical system in urban driving using deep learning techniques. A neural inference model is developed and trained via deep-learning training techniques to obtain accurate state estimates using indirect measurements of vehicle dynamic states and powertrain states. In Problem III, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at four-way unsignalized intersections with feasibility guarantees. The proposed technique involves an integration of two main decision-making layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a motion planning layer that uses low-level Model Predictive Control (MPC) principles to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angle for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands considering several vehicle dynamic constraints and ride comfort