Multimodal end-to-end autonomous driving has shown promising advancements in recent work. By embedding more modalities into end-to-end networks, the system's understanding of both static and dynamic aspects of the driving environment is enhanced, thereby improving the safety of autonomous driving. In this paper, we introduce METDrive, an end-to-end system that leverages temporal guidance from the embedded time series features of ego states, including rotation angles, steering, throttle signals, and waypoint vectors. The geometric features derived from the perception sensor data and the time series features of ego state data jointly guide the waypoint prediction with the proposed temporal guidance loss function. We evaluated METDrive on the CARLA leaderboard benchmarks, achieving a driving score of 70%, a route completion score of 94%, and an infraction score of 0.78.

Camera, LiDAR, and radar are common perception sensors for autonomous driving tasks. Robust prediction of 3D object detection is optimally based on the fusion of these sensors. Taking advantage of their abilities remains a challenge, because each of these sensors has its own characteristics. Specifically, different sensors present different scales in their corresponding extracted features. To address this problem, considering the feature alignment in different scales, in this paper, we propose FADet, a multi-sensor 3D detection network, which specifically studies the characteristics of different sensors across the dimensions of their data input based on our local featured attention modules. For camera images, we propose a dual-attention-based submodule. For LiDAR point clouds, the triple-attention-based submodule is utilized, while the mixed-attention-based submodule is applied for features of radar points. With local featured attention submodules, our FADet has effective detection results in long-tail and complex scenes from camera, LiDAR and radar input. In the NuScenes validation dataset, FADet achieves state-of-the-art performance on LiDAR-camera object detection tasks with 71.8% NDS and 69.0% mAP, at the same time, on radar-camera object detection tasks with 51.7% NDS and 40.3% mAP.