Region proposal algorithms play an important role in most state-of-the-art two-stage object detection networks by hypothesizing object locations in the image. Nonetheless, region proposal algorithms are known to be the bottleneck in most two-stage object detection networks, increasing the processing time for each image and resulting in slow networks not suitable for real-time applications such as autonomous driving vehicles. In this paper we introduce RRPN, a Radar-based real-time region proposal algorithm for object detection in autonomous driving vehicles. RRPN generates object proposals by mapping Radar detections to the image coordinate system and generating pre-defined anchor boxes for each mapped Radar detection point. These anchor boxes are then transformed and scaled based on the object's distance from the vehicle, to provide more accurate proposals for the detected objects. We evaluate our method on the newly released NuScenes dataset [1] using the Fast R-CNN object detection network [2]. Compared to the Selective Search object proposal algorithm [3], our model operates more than 100x faster while at the same time achieves higher detection precision and recall. Code has been made publicly available at this https URL .
Authors in @cite_2 discussed the application of Radars in navigation for autonomous vehicles, using an extended Kalman filter to fuse the radar and vehicle control signals for estimating vehicle position. In @cite_8 authors proposed a correlation based pattern matching algorithm in addition to a range-window to detect and track objects in front of a vehicle. In @cite_9 Ji proposed an attention selection system based on Radar detections to find candidate targets and employ a classification network to classify those objects. They generated a single attention window for each Radar detection, and used a Multi-layer In-place Learning Network (MILN) as the classifier.
@cite_8 @cite_9 @cite_2
2231187102 1489146341 2106533209
We propose an object classification system that incorporates information from a video camera and an automotive radar. The system implements three processes. The first process is attention selection, in which the radar guides a selection of a small number of candidate images for analysis by the camera and our learning method. In the second process, normalized attention windows are processed by orientation-selective feature detectors, generating a sparse representation for each window. In the final process, a multilayer in-place learning network is used to distinguish sparse representations of different objects. Though it is more flexible in terms of variety of classification tasks, the system currently demonstrates its high accuracy in comparison with others on real-world data of a two-class recognition problem. Fuzzy logic techniques have become popular to address various processes for multisensor data fusion. Examples include: (1) fuzzy membership functions for data association, (2) evaluation of alternative hypotheses in multiple hypothesis trackers, (3) fuzzy-logic-based pattern recognition (e.g., for feature-based object identification), and (4) fuzzy inference schemes for sensor resource allocation. These approaches have been individually successful but are limited to only a single subprocess within a data fusion system. At The Pennsylvania State University, Applied Research Laboratory, a general-purpose fuzzy logic architecture has been developed that provides for control of sensing resources, fusion of data for tracking, automatic object recognition, control of system resources and elements, and automated situation assessment. This general architecture has been applied to implement an autonomous vehicle capable of self-direction, obstacle avoidance, and mission completion. The fuzzy logic architecture provides interpretation and fusion of multisensor data (i.e., perception) as well as logic for process control (action). This paper provides an overview of the fuzzy logic architecture and a discussion of its application to data fusion in the context of the Department of Defense (DoD) Joint Directors of Laboratories (JDL) Data Fusion Process Model. A new, robust, fuzzy calculus is introduced. An example is provided by modeling a component of the perception processing of a bat. >