Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions

By Andreas Pfeuffer, Klaus C. J. Dietmayer. 2018

52 citations recorded by the source corpus.

A good and robust sensor data fusion in diverse weather conditions is a quite challenging task. There are several fusion architectures in the literature, e.g. the sensor data can be fused right at the beginning (Early Fusion), or they can be first processed separately and then concatenated later (Late Fusion). In this work, different fusion architectures are compared and evaluated by means of object detection tasks, in which the goal is to recognize and localize predefined objects in a stream of data. Usually, state-of-the-art object detectors based on neural networks are highly optimized for good weather conditions, since the well-known benchmarks only consist of sensor data recorded in optimal weather conditions. Therefore, the performance of these approaches decreases enormously or even fails in adverse weather conditions. In this work, different sensor fusion architectures are compared for good and adverse weather conditions for finding the optimal fusion architecture for diverse weather situations. A new training strategy is also introduced such that the performance of the object detector is greatly enhanced in adverse weather scenarios or if a sensor fails. Furthermore, the paper responds to the question if the detection accuracy can be increased further by providing the neural network with a-priori knowledge such as the spatial calibration of the sensors.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection (2018, background): “Therefore, it is natural to combine these two sensors [11]–[17].”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, background): “mean average [110], nearest neighbors [143], or bilateral filter [144].”
Multimodal End-to-End Autonomous Driving (2019, methodology): “based [10], [11], [12], [13], [14], [15], and multimodal [16], [17], [18], [19], [20], [21], [22], [23]); object tracking (imagebased [24], [25], [26], [27], [28], [29], [30], multimodal [31]); traffic sign recognition [32]; semantic segmentation (by class/category [33], [34], [35] and object instan”