Autonomous driving paper index
LIDAR–camera deep fusion for end-to-end trajectory planning of autonomous vehicle
One-line summary
In this work, we propose an attention-based framework to integrate representations of information from the two sensors and complete trajectory planning tasks using an end-to-end learning-based approach.
Engineering notes
The experiment results show that the proposed framework outperforms the models with single sensor input and the model without cross attention.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Lidars and cameras are critical sensors which offer complementary information for scene perception and trajectory planning tasks in autonomous driving. In this work, we propose an attention-based framework to integrate representations of information from the two sensors and complete trajectory planning tasks using an end-to-end learning-based approach. To fuse 3D point cloud with RGB image in the same dimension, we also utilize a gradient transformation operation to convert point cloud into grey images. For the trajectory planning task, we use an auto-regressive network consisting of GRUs to predict future trajectory of ego-vehicle. The experiment results show that the proposed framework outperforms the models with single sensor input and the model without cross attention. We demonstrate the efficacy of our approach in the CARLA driving simulator.
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