TY - JOUR AU - Yi Xiao AU - Felipe Codevilla AU - Akhil Gurram AU - Onay Urfalioglu AU - Antonio Lopez PY - 2020// TI - Multimodal end-to-end autonomous driving T2 - TITS JO - IEEE Transactions on Intelligent Transportation Systems SP - 1 EP - 11 N2 - A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality. UR - https://ieeexplore.ieee.org/document/9165167 L1 - http://refbase.cvc.uab.es/files/XCG2020.pdf UR - http://dx.doi.org/10.1109/TITS.2020.3013234 N1 - ADAS ID - Yi Xiao2020 ER -