High performance pavement markings enhancing human, camera and lidar detection

Papers presented virtually at the 41st International Southern African Transport Conference on 10-13 July 2047A Safe System Approach is built on several complementary safety layers provided by the car, the road infrastructure and improved driver behavior, through education or enforcement. Historically road markings and traffic signs have focused on the driver impact by human detection and making the infrastructure better visible in all weather and traffic situations. More recently the adaptation of the road to machine vision has become very relevant due to the developments in advanced driver-assistance systems (ADAS) and autonomous vehicles (AV). Better road markings are required to improve the confidence of ADAS, and secondly to lay the base for higher levels of vehicle automation. The General Safety Regulation in the EU already mandates ADAS in new vehicle models. In 2024 all new registered vehicles need to be equipped with several ADAS, including Lane Keeping Assist (LKA) or Lane Departure Warning (LDW) systems. The human eyes and cameras are the sensors currently used have limitations in detecting road markings under certain conditions e.g., glare from sunlight or oncoming vehicles, rain, fog, low light nighttime conditions. All-weather performing (AW) tapes contain the latest developed high optics road markings, made of a mix of higher refractive index (R.I. mix 1,9 and 2,4) beads to provide reflectivity both in dry and wet condition when compared to the conventional (traditional) paint road markings with the optics of R.I 1,5 to 1,7 that perform mainly under dry conditions. This paper covers how better or improved road markings can influence both human and machine vision, with focus on camera and Light Detection and Ranging (LiDAR) sensors. It has been determined that high performance (RI>1,7) road markings help to increase the level of detection by both camera and LiDAR sensors, as well as human eyes. Particularly an All-Weather performing road marking tape was detected from significantly longer distances in wet and rainy conditions compared to traditional markings

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor