The role of simulation in autonomous driving is becoming increasingly important due to the need for rapid prototyping and extensive testing. The use of physics-based simulation involves multiple benefits and advantages at a reasonable cost while eliminating risks to prototypes, drivers and vulnerable road users. However, there are two main limitations. First, the well-known reality gap which refers to the discrepancy between reality and simulation that prevents simulated autonomous driving experience from enabling effective real-world performance. Second, the lack of empirical knowledge about the behavior of real agents, including backup drivers or passengers and other road users such as vehicles, pedestrians or cyclists. Agent simulation is usually pre-programmed deterministically, randomized probabilistically or generated based on real data, but it does not represent behaviors from real agents interacting with the specific simulated scenario. In this paper we present a preliminary framework to enable real-time interaction between real agents and the simulated environment (including autonomous vehicles) and generate synthetic sequences from simulated sensor data from multiple views that can be used for training predictive systems that rely on behavioral models. Our approach integrates immersive virtual reality and human motion capture systems with the CARLA simulator for autonomous driving. We describe the proposed hardware and software architecture, and discuss about the so-called behavioural gap or presence. We present preliminary, but promising, results that support the potential of this methodology and discuss about future steps.
翻译:由于需要快速原型和广泛测试,模拟自主驾驶的作用变得越来越重要。物理模拟的使用涉及多种好处和优势,成本合理,同时消除原型、司机和弱势道路使用者面临的风险。然而,主要有两大局限性。第一,众所周知的现实差距,即现实与模拟之间存在差异,使模拟自主驾驶经验无法促成有效的真实世界性表现。第二,缺乏关于真实物剂行为的经验知识,包括后备驾驶员或乘客和其他道路使用者,如车辆、行人或骑自行车者。机械模拟通常是预先编程的、随机随机的概率性或基于真实数据产生的,但并不代表实际物剂与具体模拟情景相互作用的行为。在本文件中,我们提出了一个初步框架,使真实物剂与模拟环境(包括自主车辆)之间能够实时互动,并从模拟感官数据中生成合成序列,而模拟感官数据可用于培训依赖行为模型的预测系统。我们的方法通常是先期决定的、随机随机随机的概率或根据真实数据生成的概率生成的,但并不代表真实的虚拟现实现实和人类运动模型,我们将这一初步和动作的系统与我们所拟议的硬件和行为模型的预演化方法加以讨论。我们目前讨论的硬件和动作结构。