Endtoend models for selfdriving cars on UPB campus roads In autonomous driving, making the vehicle trajectory planning a learnable process (so called endtoend driving), given visual input and car motion measurements alone is an idea with longstanding tradition. In a recent project we investigate the potential to incorporate the steering predictions of an endtoend model operating under constraints of a single monocular camera input, into the planning pipeline of existing opensource frameworks for autonomous driving. We describe a process to design and train such a model given a specific geographical area with its own particularities in terms of road network, traffic and driving conditions. We investigate data augmentation techniques which improve the performance of the model. Quality and performance analysis of the model is performed based on a collected dataset in the UPB campus. The evaluation is carried out using a simulation pipeline which applies transformations to the frames in test videos such that they reflect decisions made by the model in earlier steps. This brings the system evaluation closer to out of distribution testing. Results indicate that the proposed models offers a good performance for the collected dataset.

EndtoEnd Driving Model for Steering Control of Autonomous Vehicles with Future Spatiotemporal Features Endtoend deep learning has gained considerable interests in autonomous driving vehicles in both academic and industrial fields, especially in decision making process. One critical issue in decision making process of autonomous driving vehicles is steering control. Researchers has already trained different artificial neural networks to predict steering angle with frontfacing camera data stream. However, existing endtoend methods only consider the spatiotemporal relation on a single layer and lack the ability of extracting future spatiotemporal information. In this paper, we propose an endtoend driving model based on Convolutional Long ShortTerm Memory (ConvLSTM) neural network with a Multiscale Spatiotemporal Integration (MSI) module, which aiming to encode the spatiotemporal information from different scales for steering angle prediction. Moreover, we employ future sequential information to enhance spatiotemporal features of the endtoend driving model. We demonstrate the efficiency of proposed endtoend driving model on the public Udacity dataset with comparison of some existing methods. Experimental results show that the proposed model has better performances than other existing methods, especially in some complex scenarios. Furthermore, we evaluate the proposed driving model on a realtime autonomous vehicle, and results show that the proposed driving model is able to predict the steering angle with high accuracy compared to skilled human driver.