A road lane detection and tracking algorithm is developed, especially tailored to run on high-performance heterogeneous hardware like GPUs and FPGAs in autonomous road vehicles. The algorithm was initially developed in C/C++ and was ported to OpenCL which supports computation on heterogeneous hardware.A novel road lane detection algorithm is proposed using random sampling of particles modeled as straight lines. Weights are assigned to these particles based on their location in the gradient image. To improve the computation efficiency of the lane detection algorithm, lane tracking is introduced in the form of a Particle Filter. Creation of the particles in lane detection step and prediction, measurement updates in lane tracking step are computed parellelly on GPU/FPGA using OpenCL code, while the rest of the code runs on a host CPU. The software was tested on two GPUs - NVIDIA GeForce GTX 660 Ti &amp; NVIDIA GeForce GTX 285 and an FPGA - Altera Stratix-V, which gave a computational frame rate of up to 104 Hz, 79 Hz and 27 Hz respectively. The code was tested on video streams from five different datasets with different scenarios of varying lighting conditions on the road, strong shadows and the presence of light to moderate traffic and was found to be robust in all the situations for detecting a single lane. / <p>Validerat; 20140128 (global_studentproject_submitter)</p>

Real-time lane detection or localization is a crucial problem in modern Advanced Driver Assistance Systems (ADAS), especially in Automated Driving System. This thesis estimates the possibility to implement a lane detection system in the available low-power embedded hardware. Various state-of-the-art Lane Detection algorithms are assessed based on a number of proposed criteria. From the result of the evaluation, three different algorithms are constructed and implemented in the hardware using OpenCV library. The lane detection stage is done with different methods: using Hough Transform for line detection or randomly sampling hypotheses which are straight lines or cubic splines over the pre-processed binary image. Weights of the hypotheses are then calculated based on their positions in the image. The hypothesis which has highest weight or best position will be chosen to represent lane marking. To increase the performance of the system, tracking stage is introduced with the help of Particle Filter or Kalman Filter. The systems are then tested with several different datasets to evaluate the speed, performance and ability to work in real-time. In addition, the system interfaces with CAN bus using a CAN interface, so that the output data can be sent as messages via the CAN bus to other systems.

Real-time lane detection or localization is a crucial problem in modern Advanced Driver Assistance Systems (ADAS), especially in Automated Driving System. This thesis estimates the possibility to implement a lane detection system in the available low-power embedded hardware. Various state-of-the-art Lane Detection algorithms are assessed based on a number of proposed criteria. From the result of the evaluation, three different algorithms are constructed and implemented in the hardware using OpenCV library. The lane detection stage is done with different methods: using Hough Transform for line detection or randomly sampling hypotheses which are straight lines or cubic splines over the pre-processed binary image. Weights of the hypotheses are then calculated based on their positions in the image. The hypothesis which has highest weight or best position will be chosen to represent lane marking. To increase the performance of the system, tracking stage is introduced with the help of Particle Filter or Kalman Filter. The systems are then tested with several different datasets to evaluate the speed, performance and ability to work in real-time. In addition, the system interfaces with CAN bus using a CAN interface, so that the output data can be sent as messages via the CAN bus to other systems.

Detection of multiple-lane markings (lane-line) on road surfaces is an essential aspect of autonomous vehicles. Although several approaches have been proposed to detect lanes, detecting multiple lane-lines consistently, particularly across a stream of frames and under varying lighting conditions is still a challenging problem. Since the road's markings are designed to be smooth and parallel, lane-line sampled features tend to be spatially and temporally correlated inside and between frames. In this thesis, we develop novel methods to model these spatial and temporal dependencies in the form of the target tracking problem. In fact, instead of resorting to the conventional method of processing each frame to detect lanes only in the space domain, we treat the overall problem as a Multiple Extended Target Tracking (METT) problem. In the first step, we modelled lane-lines as multiple "independent" extended targets and developed a spline mathematical model for the shape of the targets. We showed that expanding the estimations across the time domain could improve the result of estimation. We identify a set of control points for each spline, which will track over time. To overcome the clutter problem, we developed an integrated probabilistic data association fi lter (IPDAF) as our basis, and formulated a METT algorithm to track multiple splines corresponding to each lane-line.In the second part of our work, we investigated the coupling between multiple extended targets. We considered the non-parametric case and modeled target dependency using the Multi-Output Gaussian Process. We showed that considering dependency between extended targets could improve shape estimation results. We exploit the dependency between extended targets by proposing a novel recursive approach called the Multi-Output Spatio-Temporal Gaussian Process Kalman Filter (MO-STGP-KF). We used MO-STGP-KF to estimate and track multiple dependent lane markings that are possibly degraded or obscured by traffic. Our method tested for tracking multiple lane-lines but can be employed to track multiple dependent rigid-shape targets by using the measurement model in the radial space In the third section, we developed a Spatio-Temporal Joint Probabilistic Data Association Filter (ST-JPDAF). In multiple extended target tracking problems with clutter, sometimes extended targets share measurements: for example, in lane-line detection, when two-lane markings pass or merge together. In single-point target tracking, this problem can be solved using the famous Joint Probabilistic Data Association (JPDA) filter. In the single-point case, even when measurements are dependent, we can stack them in the coupled form of JPDA. In this last chapter, we expanded JPDA for tracking multiple dependent extended targets using an approach called ST-JPDAF. We managed dependency of measurements in space (inside a frame) and time (between frames) using different kernel functions, which can be learned using the trained data. This extension can be used to track the shape and dynamic of dependent extended targets within clutter when targets share measurements. The performance of the proposed methods in all three chapters are quanti ed on real data scenarios and their results are compared against well-known model-based, semi-supervised, and fully-supervised methods. The proposed methods offer very promising results. / Thesis / Doctor of Philosophy (PhD)

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