A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the "policy layer", is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the "execution layer", is a short-term optimization-based controller that tracks the reference trajecotries given by the "policy layer" with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the "policy layer" to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the "policy layer" can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework

AgileDART: An Agile and Scalable Edge Stream Processing Engine

Edge applications generate a large influx of sensor data at massive scales. Under many time-critical scenarios, these massive data streams must be processed in a very short time to derive actionable intelligence. However, traditional data processing systems (e.g., stream processing systems, cloud-based IoT data processing systems) are not well-suited for these edge applications. This is because they often do not scale well with a large number of concurrent stream queries, do not support low-latency processing under limited edge computing resources, and do not adapt to the level of heterogeneity and dynamicity commonly present in edge computing environments. These gaps suggest a need for a new edge stream processing system that advances the stream processing paradigm to achieve efficiency and flexibility under the constraints presented by edge computing architectures. We present AgileDart, an agile and scalable edge stream processing engine that enables fast stream processing of a large number of concurrently running low-latency edge applications' queries at scale in dynamic, heterogeneous edge environments. The novelty of our work lies in a dynamic dataflow abstraction that leverages distributed hash table (DHT) based peer-to-peer (P2P) overlay networks to automatically place, chain, and scale stream operators to reduce query latencies, adapt to workload variations, and recover from failures; and a bandit-based path planning model that can re-plan the data shuffling paths to adapt to unreliable and heterogeneous edge networks. We show analytically and empirically that AgileDart outperforms Storm and EdgeWise on query latency and significantly improves scalability and adaptability when processing a large number of real-world edge stream applications' queries.Comment: 18 pages, 18 figure

Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions, AVs need to not only predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such behavior prediction. In this paper, we propose an uncertain-aware integrated prediction and planning (UAPP) framework. It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties. We first propose the definitions for courtesy and confidence. Based on that, their influences on the behaviors of AVs in interactive driving scenarios are explored. Moreover, we evaluate the proposed algorithm on naturalistic human driving data by comparing the generated behavior against ground truth. Results show that the online inference can significantly improve the human-likeness of the generated behaviors. Furthermore, we find that human drivers show great courtesy to others, even for those without right-of-way. We also find that such driving preferences vary significantly in different cultures.Comment: Accepted by IEEE Robotics and Automation Letters. January 202

Courteous Autonomous Cars

Typically, autonomous cars optimize for a combination of safety, efficiency, and driving quality. But as we get better at this optimization, we start seeing behavior go from too conservative to too aggressive. The car's behavior exposes the incentives we provide in its cost function. In this work, we argue for cars that are not optimizing a purely selfish cost, but also try to be courteous to other interactive drivers. We formalize courtesy as a term in the objective that measures the increase in another driver's cost induced by the autonomous car's behavior. Such a courtesy term enables the robot car to be aware of possible irrationality of the human behavior, and plan accordingly. We analyze the effect of courtesy in a variety of scenarios. We find, for example, that courteous robot cars leave more space when merging in front of a human driver. Moreover, we find that such a courtesy term can help explain real human driver behavior on the NGSIM dataset.Comment: International Conference on Intelligent Robots (IROS) 201