Point Grid Map-Based Mid-To-Mid Driving without Object Detection

Abstract

Teaching autonomous vehicles to imitate human driving in complex, urban traffic scenarios is a difficult task. “End-to-end” autonomous driving systems, based on “imitation learning”, are an expecting approach. A model learns the relationships between sensing input and vehicle control signal outputs. These methods can successfully achieve driving in simple scenarios such as lane keeping. In contrast, the “mid-to-mid” autonomous driving methods now being proposed. In such framework, the model learns the relationships between pre-processed feature maps from the model-based system as input and the future position of the ego vehicle as the output. Mid-to-mid driving methods can direct vehicles more robustly than end-to-end driving methods in some complex driving environments. However, mid-to-mid driving methods use the results of the object detection module to create the feature map. If object detection fails, or detection performance is poor due to changes in the driving environment, prediction performance may also be degraded. Our proposed method uses a prediction module that outputs point grid maps directly, without the use of an object detection module, which are then incorporated into the feature map. Point grid maps represent the locations of surrounding vehicles and obstacles directly, based on LiDAR point cloud data. Since the results of object detection are not used by the prediction module, detection performance does not affect prediction performance. In this study we conduct two experiments, an off-line evaluation using a Lyft dataset, and an on-line evaluation using the CARLA simulator. The results show that our model can achieve the same level of ego-vehicle position prediction performance as a model using annotated object location information.