A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions. Recent state-of-the-art solutions addressing this challenge include deep learning techniques as they provide end-to-end solution to predict steering angles directly from the raw input images with higher accuracy. Most of these works ignore the temporal dependencies between the image frames. In this paper, we tackle the problem of utilizing multiple sets of images shared between two autonomous vehicles to improve the accuracy of controlling the steering angle by considering the temporal dependencies between the image frames. This problem has not been studied in the literature widely. We present and study a new deep architecture to predict the steering angle automatically by using Long-Short-Term-Memory (LSTM) in our deep architecture. Our deep architecture is an end-to-end network that utilizes CNN, LSTM and fully connected (FC) layers and it uses both present and futures images (shared by a vehicle ahead via Vehicle-to-Vehicle (V2V) communication) as input to control the steering angle. Our model demonstrates the lowest error when compared to the other existing approaches in the literature.
@cite_18: The paper discusses autocalibration, object detection, and object tracking for unmanned surface vehicles. Input data are recorded with a wide-baseline stereo vision system providing accuracy for distance estimations. The paper reports about followed ways and novel contributions for ensuring a working system solution. Automatic self-calibration is used for the wide-baseline stereo vision system. Robust sea surface estimation and the detection of the horizon support the understanding of the given scene environment. Long-range (i.e. up to 500 m) object detection and tracking are supported by the used wide-baseline stereo system. The paper informs about the complete system design, informs about applied or designed methods, and also about experiments which verify that the system achieved an operational state.
@cite_16: In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and system-compliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of ver...
@cite_7: In this paper, the implementation of image recognition for traffic light signal recognition system is demonstrated. The detection of traffic light signal is an essential step for a self-driving car. Here we present a method for the recognition of traffic lights using image processing and controlling the vehicle accordingly. The algorithm developed in this research work is tested and processed using a Raspberry Pi board. The input-output modules such as camera, motors and chassis of the model car are all integrated together so they can perform as a single unit. For processing the image on real-time, OpenCV is used as an API to perform essential steps in the detection of signal like capturing, resizing, thresholding and morphological operations. Contour detection on a binary image has further been used for object detection. The algorithm has been tested with Valgrind profiling tools Callgrind and Cachegrind.
The problem of navigating self-driving car by utilizing the perception acquired from sensory data has been studied in the literature with and without using end-to-end approaches @cite_16 . For example the works from @cite_7 , @cite_18 use multiple components for recognizing objects of safe-driving concerns, such as lanes, vehicles, traffic signs and pedestrians. The recognition results are then combined to give a reliable world representation, which are used with an Artificial Intelligence (AI) system to make decisions and control the car.