Automatic detection of road markings will enhance the capabilities of Advanced Driver Assistance Systems (ADAS) as road markings denote vital information pertaining to traffic safety and navigation. However, little work has been done in the area of vision-based detection of road markings in general, and existing literature was found to be largely confined to lane marking detection. In this thesis, algorithms and architectures have been proposed to identify road markings that are categorized into (a) basic linear markings (BLM) (b) complex linear markings (CLM) (c) arrow markings and (d) pedestrian markings. The identification of BLM such as dashed and solid lane markings has been tackled first. An efficient feature extraction process based on gradient angle histograms was introduced for shortlisting potential lane marking candidates in a block-based manner, resulting in customized edge maps (called Straight Line Edge Map or SLEM). A study on the relationship between the block-size and the quality of SLEM was conducted to identify appropriate block settings leading to successful extraction of lane markings despite noisy edge pixels such as tree shadows. The outlier removal step involves decomposing the SLEM into positive and negative edges based on intensity transition characteristics, which are then subjected to the Hough Transform (HT). The thickness criterion was also introduced during the detection of HT peaks to eliminate artifacts resembling lane markings. The proposed GAH based preprocessing approach is shown to reduce the number of HT computations by more than 50%, due to a notable reduction in the number of edge pixels and angle range for HT. It is shown that the proposed lane marking detection technique yields high detection accuracy, ranging from 98% to 99%, upon validation on an extensive dataset with more than 6,000 image frames representing different illumination, weather and complex road conditions. In order to distinguish solid and dashed single lane markings, a two-pronged technique based on continuity analysis of the lane markings in the spatial and temporal domains was proposed. It was shown that this method successfully distinguishes dashed and solid lane markings for all the test sequences considered in the dataset. The proposed BLM detection module can be readily configured to detect horizontal BLMs such as stop lines. A generic version of the lane marking detection algorithm was realized to detect BLMs for a given thickness. A novel parallel method for HT computations called Additive Hough transform (AHT) was proposed to drastically collapse the complexity of HT computations by replacing the trigonometric operations with simple additions. It relies on dividing the image into uniform blocks, and processing them in parallel, by exploiting the proposed additive property of HT. A study on how block size affects the compute efficiency was conducted to show that AHT reduces the total computation time by at least k2 times as compared to existing HT architectures for a k  k grid,…