The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the real world, we need to evaluate them in closed-loop. Towards this goal, we propose to leverage the wealth of interesting scenarios captured in the real world and make them reactive and controllable to enable closed-loop SDV evaluation in what-if situations. In particular, we present MIXSIM, a hierarchical framework for mixed reality traffic simulation. MIXSIM explicitly models agent goals as routes along the road network and learns a reactive route-conditional policy. By inferring each agent's route from the original scenario, MIXSIM can reactively re-simulate the scenario and enable testing different autonomy systems under the same conditions. Furthermore, by varying each agent's route, we can expand the scope of testing to what-if situations with realistic variations in agent behaviors or even safety critical interactions. Our experiments show that MIXSIM can serve as a realistic, reactive, and controllable digital twin of real world scenarios. For more information, please visit the project website: https://waabi.ai/research/mixsim/

As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible autonomy failures, such as perception failures due to occlusion. In this paper, we propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system. Given an initial traffic scenario, AdvSim modifies the actors’ trajectories in a physically plausible manner and updates the LiDAR sensor data to match the perturbed world. Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack. Our experiments show that our approach is general and can identify thousands of semantically meaningful safety-critical scenarios for a wide range of modern self-driving systems. Furthermore, we show that the robustness and safety of these systems can be further improved by training them with scenarios generated by AdvSim.

Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems. Sharing information and distributing workloads allow autonomous agents to better perform tasks and increase computation efficiency. However, shared information can be modified to execute adversarial attacks on deep learning models that are widely employed in modern systems. Thus, we aim to study the robustness of such systems and focus on exploring adversarial at-tacks in a novel multi-agent setting where communication is done through sharing learned intermediate representations of neural networks. We observe that an indistinguishable adversarial message can severely degrade performance, but becomes weaker as the number of benign agents increases. Furthermore, we show that black-box transfer attacks are more difficult in this setting when compared to directly perturbing the inputs, as it is necessary to align the distribution of learned representations with domain adaptation. Our work studies robustness at the neural network level to con-tribute an additional layer of fault tolerance to modern security protocols for more secure multi-agent systems.

Modern self-driving systems heavily rely on deep learning. As a consequence, their performance is influenced significantly by the quality and richness of the training data. Data collection platforms can generate many hours of raw data on a daily basis, however, it is not feasible to label everything. Therefore, it is critical to have a mechanism to identify "what to label". Active learning approaches identify examples to label, but their interestingness is tied to a fixed model performing a particular task. These assumptions are not valid in self-driving, where we must solve a diverse set of tasks (i.e., perception, motion forecasting, and planning) and models frequently evolve over time. In this paper, we introduce a novel approach to dataset selection that exploits a diverse set of criteria that quantize interestingness of traffic scenes. Our experiments on a wide range of tasks and models demonstrate that the proposed curation pipeline is able to select datasets that lead to better generalization and improved performance.

Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Despite the fact that this poses a security concern for the self-driving industry, there has been very little exploration in terms of 3D perception, as most adversarial attacks have only been applied to 2D flat images. In this paper, we address this issue and present a method to generate universal 3D adversarial objects to fool LiDAR detectors. In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%. We report attack results on a suite of detectors using various input representation of point clouds. We also conduct a pilot study on adversarial defense using data augmentation. This is one step closer towards safer self-driving under unseen conditions from limited training data.

Modern data parallel frameworks, such as Apache Spark, are designed to execute complex data processing jobs that contain a large number of tasks, with dependencies between these tasks represented by a directed acyclic graph (DAG). When scheduling these tasks, the ultimate objective is to minimize the makespan of the schedule, which is equivalent to minimizing the job completion time. With task dependencies, however, minimizing the makespan of the schedule is non-trivial, especially when tasks in the DAG have different resource demands with respect to multiple resource types. In this paper, we present Spear, a new scheduling framework designed to minimize the makespan of complex jobs, while considering both task dependencies and heterogeneous resource demands at the same time. Inspired by recent advances in artificial intelligence, Spear applies Monte Carlo Tree Search (MCTS) in the specific context of task scheduling, and trains a deep reinforcement learning model to guide the expansion and rollout steps in MCTS. With deep reinforcement learning, search efficiency can be significantly improved by focusing on more promising branches. With both simulations and experiments using traces from production workloads, we compare the scheduling performance of Spear with state-of-the-art job schedulers in the literature, and Spear can outperform those approaches by up to 20%. Our results have validated our claims that MCTS and deep reinforcement learning can readily be applied to optimize the scheduling of complex jobs with task dependencies.