Insertion of real agents behaviors in CARLA autonomous driving simulator

TL;DR

This paper introduces a framework combining virtual reality and human motion capture with the CARLA simulator to incorporate real agent behaviors, aiming to improve simulation realism and training data quality for autonomous driving.

Contribution

It presents a novel integration of immersive VR and motion capture with CARLA to simulate real agent behaviors in autonomous driving scenarios.

Findings

Preliminary results show the feasibility of real-time interaction between real agents and simulation.

The approach generates synthetic sensor data reflecting real human behaviors.

Future work will enhance behavioral realism and system robustness.

Abstract

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.