Implementation and Validation of Behavior Cloning Using Scaled Vehicles

Abstract

<div class="section abstract"><div class="htmlview paragraph">Recent trends in autonomy have emphasized end-to-end deep-learning-based methods that have shown a lot of promise in overcoming the requirements and limitations of feature-engineering. However, while promising, the black-box nature of deep-learning frameworks now exacerbates the need for testing with end-to-end deployments. Further, as exemplars of systems-of-systems, autonomous vehicles (AVs) engender numerous interconnected component-, subsystem and system-level interactions. The ensuing complexity creates challenges for verification and validation at the various component, subsystem- and system-levels as well as end-to-end testing. While simulation-based testing is one promising avenue, oftentimes the lack of adequate fidelity of AV and environmental modeling limits the generalizability. In contrast, full-scale AV testing presents the usual limitations of time-, space-, and cost. Hence in this paper, we explore the opportunity for using experiential learning possible with a scaled vehicle-based deployment to overcome the limitations(e.g. simulation fidelity or experimentation costs) of scaled vehicles to lower the barriers especially at the early stages of testing of autonomy algorithms.</div><div class="htmlview paragraph">In recent times, several efforts have emerged for testing deep-learning-based autonomy algorithms on scaled vehicles - the Nvidia Jet racer, Amazon Deep racer, and Donkey car are being widely used. In this paper, we examine a deployment of the Donkey car Behavior Cloning software stack on a 1/10<sup>th</sup> scaled vehicle (F1tenth) and the issues faced while deploying the other software stacks. In particular, we explored the effectiveness of: (i) mixing and matching frameworks; and (ii) use of scaled vehicles in an academic set up to support testing and deployment of supervised learning (behavior cloning) technique to achieve lane-keeping and obstacle-avoidance. We showcase that the use of this scaled-vehicle framework permitted the rapid exploration of many different test tracks (challenging with full-scale vehicle tests) while retaining realistic environmental conditions (challenging with simulation-alone testing).</div></div>

Topics & Concepts