ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling

Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially accelerate autonomous driving research, especially for perception tasks such as 3D detection and trajectory forecasting. Since the driving logs in these datasets contain HD maps and detailed object annotations which accurately reflect the real-world complexity of traffic behaviors, we can harvest a massive number of complex traffic scenarios and recreate their digital twins in simulation. Compared to the hand-crafted scenarios often used in existing simulators, data-driven scenarios collected from the real world can facilitate many research opportunities in machine learning and autonomous driving. In this work, we present ScenarioNet, an open-source platform for large-scale traffic scenario modeling and simulation. ScenarioNet defines a unified scenario description format and collects a large-scale repository of real-world traffic scenarios from the heterogeneous data in various driving datasets including Waymo, nuScenes, Lyft L5, and nuPlan datasets. These scenarios can be further replayed and interacted with in multiple views from Bird-Eye-View layout to realistic 3D rendering in MetaDrive simulator. This provides a benchmark for evaluating the safety of autonomous driving stacks in simulation before their real-world deployment. We further demonstrate the strengths of ScenarioNet on large-scale scenario generation, imitation learning, and reinforcement learning in both single-agent and multi-agent settings. Code, demo videos, and website are available at https://metadriverse.github.io/scenarionet

Designing a Cockpit Functionalities Architecture for Trajectory Based Operations

Trajectory Based Operations (TBO) will require new procedures and systems to achieve a suitable automation of air traffic operations. Procedures and systems for automated operations are closely related and therefore frequently they need to be modeled in a combined way. Our group is currently employing recent agent-oriented methodological approaches to obtain conceptual models about TBO scenarios. Conceptual models define roles of air traffic entities as well as their interactions together with a detailed description of the entities’ architecture and dynamic behaviour. In this paper we present a cockpit functionality architecture built upon a methodological analysis and design of a TBO scenario as a multi-agent system. The proposed design has the advantage of mapping to an executable model for analytical simulation of TBO concepts and its modular architecture allows for a progressive integration of additional underlying models with specific functionalities

Analysis of Key Installation Protection using Computerized Red Teaming

This paper describes the use of genetic algorithms (GAs) for computerized red teaming applications, to explore options for military plans in specific scenarios. A tool called Optimized Red Teaming (ORT) is developed and we illustrate how it may be utilized to assist the red teaming process in security organizations, such as military forces. The developed technique incorporates a genetic algorithm in conjunction with an agent-based simulation system (ABS) called MANA (Map Aware Non-uniform Automata). Both enemy forces (the red team) and friendly forces (the blue team) are modelled as intelligent agents in a multi-agent system and many computer simulations of a scenario are run, pitting the red team plan against the blue team plan. The paper contains two major sections. First, we present a description of the ORT tool, including its various components. Second, experimental results obtained using ORT on a specific military scenario known as Key Installation Protection, developed at DSO National Laboratories in Singapore, are presented. The aim of these experiments is to explore the red tactics to penetrate a fixed blue patrolling strategy

Distributed agent-based building evacuation simulator

The optimisation of the evacuation of a building plays a fundamental role in emergency situations. The behaviour of individuals, the directions that civilians receive, and the actions of the emergency personnel, will affect the success of the operation. We describe a simulation system that represents the individual, intelligent, and interacting agents that cooperate and compete while evacuating the building. The system also takes into account detailed information about the building and the sensory capabilities that it may contain. Since the level of detail represented in such a simulation can lead to computational needs that grow at least as a polynomial function of the number of the simulated agents, we propose an agent-oriented Distributed Building Evacuation Simulator (DBES). The DBES is integrated with a wireless sensor network which offers a closed loop representation of the evacuation procedure, including the sensed data and the emergency decision making