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Understanding Curbs and Intersections for Effective On-Road DrivingAuthor: danish.sodhi Date: 2018-08-21 Report no: IIIT/TH/2018/65 Advisor:Madhava Krishna AbstractLast few years have seen a tremendous increase in interest for autonomous driving. As advances have been made in this field, autonomous driving has faced various challenges. In this thesis, we present vision based solutions to curb and intersection detection problems faced by self-driving cars. In the first part of the thesis, we present a discriminative approach to the problem of curb detection under diverse road conditions. Curb detection is a critical component of driver assistance and autonomous driving systems. We define curbs as the intersection of drivable and non-drivable area which are classified using dense Conditional random fields(CRF). In our method, we fuse output of a neural network used for pixel-wise semantic segmentation with depth and color information from stereo cameras. CRF fuses the output of a deep model and height information available in stereo data and provides improved segmentation. Further, we introduce temporal smoothness using a weighted average of Segnet output and output from a probabilistic voxel grid as our unary potential. Finally, we show improvements over the current state of the art neural networks. Our proposed method shows accurate results over a large range of variations in curb curvature and appearance, without the need of retraining the model for the specific dataset. While it is important to detect road boundaries, another interesting problem is to know how the boundaries fork and lead to an intersection. In the second part of the thesis, we focus on the problem of road intersection detection. Long-short term memory networks(LSTM) models have shown considerable performance on a variety of problems dealing with sequential data. In this thesis, we propose a variant of Long-Term Recurrent Convolutional Network(LRCN) to detect road intersection. We call this network as IntersectNet. We pose road intersection detection as binary classification task over a sequence of frames. The model combines deep hierarchical visual feature extractor with recurrent sequence model. The model is end to end trainable with a capability of capturing the temporal dynamics of the system. We exploit this capability to identify road intersection in a sequence of temporally consistent images. The model has been rigorously trained and tested on various different datasets. We think that our findings could be useful to model behavior of autonomous agent in the real world. Full thesis: pdf Centre for Robotics |
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