Ova 2dataset due to the fact it perceives the curb in the sidewalk as a lane.MATLABReal time implementation on the proposed algorithmData from south Korea road and Caltech dataset.IMU sensors may be incorporated to avoid the false detection of lanes.[44]YYRobust lane detection strategy by using a monocular camera in which the roads are offered with proper lane markings.Overall performance drops when road is not flatSoftware based functionality analysis on Caltech dataset for various urban driving situation. Hardware implementation on the Tuyou autonomous car.—Caltech and custom-made datasetDue towards the difficulty In image capturing false detection occurred. Additional training or inclusion of sensors for live dataset collection will support to mitigate it.[45]YOverall strategy test algorithm inside 33 ms per frame.Require to cut down computational complexity by using vanishing point and GYKI 52466 Epigenetics adaptive ROI for every single frame.Below different Illumination situation lane detection price with the algorithm is an average 93Software based analysis carried out.There are possibilities, to test algorithm at day time with inclement weather situations.Custom data primarily based on Real-time–[46]YBetter accuracy for sharp curve lanes.The suitability from the algorithm for various road geometrics however to study.The results show that the accuracy of lane detection is about 97 and also the typical time taken to detect the lane is 20 ms.Custom produced simulator C/C and visual studio–Custom data–Sustainability 2021, 13,17 ofTable 4. Cont.Data Simulation Sources Strategy Benefits Drawbacks Result Tool Utilised Future Prospects Information Purpose for DrawbackReal[47]Yvanishing point detection approach for unstructured roads Proposed a lane detection approach applying Gaussian distribution random sample consensus (G-RANSAC), usage of rider detector to extract the functions of lane points and adaptable neural network for take away noise.Accurate and robust overall performance for unstructured roads.Tough to acquire robust vanishing point for detection of lane for unstructured scene.The accuracy of vanishing point range between 80.9 to 93.six for distinct scenarios. The proposed algorithm is tested below various illumination situation ranging from typical, intense, standard and poor and offers lane detection accuracy as 95 , 92 , 91 and 90 .Unmanned ground automobile and mobile robot.Future scope for structured roads with distinctive scenarios.Custom dataComplex background BMS-986094 Anti-infection interference and unclear road marking.[48]YProvides improved results during the presence of car shadow and minimal illumination of your atmosphere.—Software based analysisNeed to test proposed technique under several times like day, night.Test vehicle—Table five. A comprehensive summary of robust lane detection and tracking.Data Simulation Sources Process Employed Benefits Drawbacks Outcome Tool Utilised Future Prospects Information Purpose for DrawbacksReal[49]YInverse perspective mapping approach is applied to convert the image to bird’s eye view.Quick detection of lane.The algorithm overall performance drops due to the fluctuation within the lighting circumstances.The lane detection error is five . The cross-track error is 25 lane detection time is 11 ms.Fisheye dashcam: inertial measurement unit; Arm processor-based laptop.Enhancing the algorithm appropriate for complex road scenario and with significantly less light circumstances.Information obtained by utilizing a model automobile operating at a speed of 1 m/sThe complicated environment creates unnecessary tilt causing some inaccuracy in lane detection.Sustainability 2021, 13,18 ofTable 5. Cont.Information Simula.