[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84548-en":3,"doc-seo-84548-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84548,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",6,"Technology","Typography-Based Monocular Distance Estimation for Advanced Driver-Assistance Systems","Estimating the distance to a leading vehicle is essential for forward collision warning, adaptive cruise control, and automated emergency braking. Instead of relying on costly radar, LiDAR, or stereo depth, the work proposes Typography-Based Monocular Distance Estimation using the rear license plate as a standardized physical target. By detecting the plate, measuring character height and additional typography cues, and selecting regulatory character dimensions by issuing state, the method recovers distance from camera geometry. It also provides rate of change and time-to-collision, applies frame smoothing, and supports fault-tolerant perception. Reported distance error is below 0.13 m.","TYPOGRAPHY-BASED MONOCULAR DISTANCE ESTIMATION FOR ADVANCED DRIVER-ASSISTANCE SYSTEMS  \narXiv :2607 .003 19v 1 [ cs .CV] 1 Jul 2026  \nManognya Lokesh Reddy  \nUniversity of Michigan-Dearborn Dearborn, MI 48128 [manognya@umich.edu](manognya@umich.edu)  \nZheng Liu  \nUniversity of Michigan-Dearborn Dearborn, MI 48128 [zhengtl@umich.edu](zhengtl@umich.edu)  \nJuly 2, 2026  \nABSTRACT  \nEstimating the distance to a leading vehicle is a basic input to forward collision warning, adaptive cruise control, and automated emergency braking. Production systems obtain this distance from radar, laser scanners, or stereo camera pairs, which add cost, power draw, and packaging constraints. This paper asks whether a single ordinary camera can recover the same distance by using a target that is standardized in size and present on every road vehicle: the rear license plate. U.S. plates share a fixed outer size and a character height that is set by regulation and varies only narrowly between states, so the height of a plate character in the image is a direct measure of distance once the camera geometry is known. The proposed method (Typography-Based Monocular Distance Estimation) detects the plate, measures the height of its printed characters, identifies the issuing state to select the correct physical character height, and recovers distance from the camera projection. Three measurements taken from the same plate: the character height, the stroke width, and the character spacing. Together with the spacing of the two mounting holes and a single-image depth network, are combined so that a weak or corrupted measurement is given less weight automatically. The distance, its rate of change, and a time-to-collision estimate are smoothed across frames and used to raise a warning with the timing used by U.S. collision-warning regulations. The same plate that anchors the scale also identifies the vehicle, so the method returns a distance, a bearing, and an identity from one passive sensor. It reads scale from a printed standard instead of from time of flight or parallax, making it a cheap, low-maintenance complement to those sensors in a fault-tolerant perception stack, achieving the cost-effective distance estimation with error less than 0 . 13 m.  \n1 Introduction  \nKnowing how far away the surrounding vehicles are is fundamental to connected and automated vehicles. The distance to a leading vehicle drives longitudinal control such as adaptive cruise control, it sets the trigger point of active-safety functions such as forward collision warning (FCW) and automated emergency braking, and it feeds the situational picture used for path planning and decision making. This one number has to be both accurate and timely: a control law that brakes or releases the throttle acts on it directly, and a warning that comes late is of little use. The sensors used for this task in production, automotive radar and light detection and ranging (LiDAR), measure distance directly and accurately, but they add cost, electrical power, heat, and packaging constraints to the vehicle, and radar in particular resolves lateral position poorly, so it can report a closing object without resolving which lane that object is in. Stereo cameras recover dense depth from two synchronized views, but they need a rigid mechanical baseline and an extrinsic calibration between the two cameras that is hard to keep stable across temperature swings and over a vehicle’s service life. A single camera, by contrast, is already fitted to most new vehicles for lane keeping, traffic-sign recognition [1], and driver monitoring; it is inexpensive, draws little power, and is mechanically simple. Its one fundamental weakness is that a single image does not by itself fix absolute scale: the same pattern of pixels can be produced by a small object that is near or a large object that is far, so distance in meters cannot be recovered from one image without an extra assumption that supplies a real-world length [2, 3] .","cbCaiglXLXpiEPfe","https://ap.wps.com/l/cbCaiglXLXpiEPfe","pdf",3305164,1,24,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does Typography-Based Monocular Distance Estimation address?\",\"answer\":\"It targets accurate, timely distance estimation to the leading vehicle for safety functions such as forward collision warning, adaptive cruise control, and automated emergency braking.\"},{\"question\":\"How does the method recover distance using only a single camera?\",\"answer\":\"It detects the rear license plate, measures the height of its printed characters, determines the issuing state for correct physical character dimensions, and computes distance using camera projection geometry.\"},{\"question\":\"Why does the approach improve robustness compared with relying on a single sensing principle?\",\"answer\":\"Because distance scale comes from a printed standard rather than time-of-flight, parallax, or a learned network, it fails in different conditions (e.g., missing or unreadable plates), complementing radar, LiDAR, and learned depth in a fault-tolerant 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problem does Typography-Based Monocular Distance Estimation address?","Question",{"text":75,"@type":76},"It targets accurate, timely distance estimation to the leading vehicle for safety functions such as forward collision warning, adaptive cruise control, and automated emergency braking.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method recover distance using only a single camera?",{"text":80,"@type":76},"It detects the rear license plate, measures the height of its printed characters, determines the issuing state for correct physical character dimensions, and computes distance using camera projection geometry.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the approach improve robustness compared with relying on a single sensing principle?",{"text":84,"@type":76},"Because distance scale comes from a printed standard rather than time-of-flight, parallax, or a learned network, it fails in different conditions (e.g., missing or unreadable plates), 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