Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection

In transportation networks, where traffic lights have traditionally been used for vehicle coordination, intersections act as natural bottlenecks. A formidable challenge for existing automated intersections lies in detecting and reasoning about uncertainty from the operating environment and human-driven vehicles. In this paper, we propose a risk-aware intelligent intersection system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs). We cast the problem as a novel class of Multi-agent Chance-Constrained Stochastic Shortest Path (MCC-SSP) problems and devise an exact Integer Linear Programming (ILP) formulation that is scalable in the number of agents' interaction points (e.g., potential collision points at the intersection). In particular, when the number of agents within an interaction point is small, which is often the case in intersections, the ILP has a polynomial number of variables and constraints. To further improve the running time performance, we show that the collision risk computation can be performed offline. Additionally, a trajectory optimization workflow is provided to generate risk-aware trajectories for any given intersection. The proposed framework is implemented in CARLA simulator and evaluated under a fully autonomous intersection with AVs only as well as in a hybrid setup with a signalized intersection for HVs and an intelligent scheme for AVs. As verified via simulations, the featured approach improves intersection's efficiency by up to $200\%$ while also conforming to the specified tunable risk threshold

Hierarchical Constrained Stochastic Shortest Path Planning via Cost Budget Allocation

Stochastic sequential decision making often requires hierarchical structure in the problem where each high-level action should be further planned with primitive states and actions. In addition, many real-world applications require a plan that satisfies constraints on the secondary costs such as risk measure or fuel consumption. In this paper, we propose a hierarchical constrained stochastic shortest path problem (HC-SSP) that meets those two crucial requirements in a single framework. Although HC-SSP provides a useful framework to model such planning requirements in many real-world applications, the resulting problem has high complexity and makes it difficult to find an optimal solution fast which prevents user from applying it to real-time and risk-sensitive applications. To address this problem, we present an algorithm that iteratively allocates cost budget to lower level planning problems based on branch-and-bound scheme to find a feasible solution fast and incrementally update the incumbent solution. We demonstrate the proposed algorithm in an evacuation scenario and prove the advantage over a state-of-the-art mathematical programming based approach

QSRNet: Estimating Qualitative Spatial Representations from RGB-D Images

© 2020 IEEE. Humans perceive and describe their surroundings with qualitative statements (e.g., "Alice's hand is in contact with a bottle."), rather than quantitative values (e.g., 6-D poses of Alice's hand and a bottle). Qualitative spatial representation (QSR) is a framework that represents the spatial information of objects in a qualitative manner. Region connection calculus (RCC), qualitative trajectory calculus (QTC), and qualitative distance calculus (QDC) are some popular QSR calculi. With the recent development of computer vision, it is important to compute QSR calculi from the visual inputs (e.g., RGB-D images). In fact, many QSR application domains (e.g., human activity recognition (HAR) in robotics) involve visual inputs. We propose a qualitative spatial representation network (QSRNet) that computes the three QSR calculi (i.e., RCC, QTC, and QDC) from the RGB-D images. QSRNet has the following novel contributions. First, QSRNet models the dependencies among the three QSR calculi. We introduce the dependencies as kinematics for QSR because they are analogous to the kinematics in classical mechanics. Second, QSRNet applies the 3-D point cloud instance segmentation to compute the QSR calculi. The experimental results show that QSRNet improves the accuracy in comparison to the other state-of-the-art techniques