Autonomous Vehicle (AV) technology has been heavily researched and sought after, yet there are no SAE Level 5 AVs available today in the marketplace. We contend that over-reliance on machine learning technology is the main reason. Use of automated commonsense reasoning technology, we believe, can help achieve SAE Level 5 autonomy. In this paper, we show how automated common-sense reasoning technology can be deployed in situations where there are not enough data samples available to train a deep learning-based AV model that can handle certain abnormal road scenarios. Specifically, we consider two situations where (i) a traffic signal is malfunctioning at an intersection and (ii) all the cars ahead are slowing down and steering away due to an unexpected obstruction (e.g., animals on the road). We show that in such situations, our commonsense reasoning-based solution accurately detects traffic light colors and obstacles not correctly captured by the AV's perception model. We also provide a pathway for efficiently invoking commonsense reasoning by measuring uncertainty in the computer vision model and using commonsense reasoning to handle uncertain scenarios. We describe our experiments conducted using the CARLA simulator and the results obtained. The main contribution of our research is to show that automated commonsense reasoning effectively corrects AV-based object detection misclassifications and that hybrid models provide an effective pathway to improving AV perception.

翻译：自动驾驶车辆（AV）技术已被广泛研究并备受追捧，但目前市场上尚无SAE 5级自动驾驶车辆。我们认为，过度依赖机器学习技术是主要原因。我们相信，采用自动化常识推理技术有助于实现SAE 5级自动驾驶。本文展示了如何在缺乏足够数据样本训练基于深度学习的自动驾驶模型以应对特定异常道路场景时，部署自动化常识推理技术。具体而言，我们研究了两种情境：（i）交叉路口交通信号灯故障；（ii）前方所有车辆因突发障碍物（如道路上出现动物）而减速并转向避让。我们证明在此类情境中，基于常识推理的解决方案能准确检测出自动驾驶感知模型未能正确识别的交通灯颜色与障碍物。我们还提出了一种通过测量计算机视觉模型的不确定性并利用常识推理处理不确定场景的高效调用常识推理的路径。我们描述了使用CARLA模拟器进行的实验及获得的结果。本研究的主要贡献在于证明自动化常识推理能有效修正基于自动驾驶的目标检测误分类，且混合模型为提升自动驾驶感知能力提供了有效途径。