Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field testing. Most testing techniques consider virtualized SDCs within a simulation environment, whereas less effort has been directed towards assessing whether such techniques transfer to and are effective with a physical real-world vehicle. In this paper, we leverage the Donkey Car open-source framework to empirically compare testing of SDCs when deployed on a physical small-scale vehicle vs its virtual simulated counterpart. In our empirical study, we investigate the transferability of behavior and failure exposure between virtual and real-world environments on a vast set of corrupted and adversarial settings. While a large number of testing results do transfer between virtual and physical environments, we also identified critical shortcomings that contribute to the reality gap between the virtual and physical world, threatening the potential of existing testing solutions when applied to physical SDCs.

翻译：安全部署自驾驶车(SDC)需要彻底的模拟和实地测试。大多数测试技术都考虑模拟环境中虚拟化的SDC,而较少努力评估这些技术是否转让给实体世界车辆,是否与实体世界车辆有效。在本文中,我们利用Donkey汽车开放源头框架,在部署在实体小型车辆时对SDC的测试进行经验性比较,与虚拟模拟对等车辆进行试验。在实证研究中,我们研究了在大量腐败和敌对环境中虚拟与现实世界环境之间行为与失败接触的可转移性。虽然大量测试结果确实在虚拟环境与实体环境之间转移,但我们也发现了有助于虚拟世界与实体世界之间现实差距的重大缺陷,在应用到实体SDC时威胁现有测试解决方案的潜力。