Improved LaneNet for Lane Detection

Lane detection is a critical component in autonomous vehicles and advanced driver assistance systems (ADAS), enabling accurate lane tracking and vehicle positioning. While traditional lane detection methods based on handcrafted features and heuristics have limitations in challenging environments, the adoption of machine learning (ML) techniques has shown promise. However, many existing ML models struggle with detecting a variable number of lanes, making them less effective in complex driving scenarios. A simple solution involves the use of High Definition (HD) Maps. HD Maps offer comprehensive road information necessary for autonomous driving, but their high cost and inflexibility pose challenges for frequent updates and modifications. This research proposes an innovative approach, the Improved LaneNet (ILaneNet) network, to strike a balance between ML techniques and HD maps. By augmenting input images with a lane parameter namely the number of lanes, we aim to enhance lane detection accuracy without incurring the prohibitive costs of HD maps. ILaneNet seeks to achieve real-time precision in locating and tracking lane markings, even in challenging conditions like inadequate lighting and intricate road layouts. The objective of this study is to develop a flexible, cost-effective, and robust lane detection system that adapts to diverse driving scenarios. By incorporating pertinent information into the network, we demonstrate improved adaptability and potential advancements in autonomous driving technologies. We also introduce new evaluation metrics namely capacity, lost capacity and unsafe driving measure to assess lane detection techniques more comprehensively. We also propose evaluation of lane detection techniques by using a lane abstraction approach instead of the traditional line abstraction method. Through extensive evaluation and comparisons, we showcase the superiority of ILaneNet over LaneNet in detecting lanes. This research contributes to bridging the gap between ML techniques and HD maps, offering a viable solution for effective and efficient lane detection in autonomous vehicles and ADAS

Multi-Lane Perception Using Feature Fusion Based on GraphSLAM

An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles

A Flexible Modeling Approach for Robust Multi-Lane Road Estimation

A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical functions for the geometrical description of lanes. In addition to the current measurement data, the previously estimated result as well as additional constraints to reflect parallelism and continuity of traffic lanes, are considered in the optimization process. As evaluation of the lane estimation method, its performance is showcased using cubic splines for the geometric representation of lanes in simulated scenarios and measurements recorded using a development vehicle. In a comparison to ground truth data, robustness and precision of the lanes estimated up to a distance of 120 m are demonstrated. As a part of the environmental modeling, the presented method can be utilized for longitudinal and lateral control of autonomous vehicles

Characterizing driving behavior using automatic visual analysis

In this work, we present the problem of rash driving detection algorithm using a single wide angle camera sensor, particularly useful in the Indian context. To our knowledge this rash driving problem has not been addressed using Image processing techniques (existing works use other sensors such as accelerometer). Car Image processing literature, though rich and mature, does not address the rash driving problem. In this work-in-progress paper, we present the need to address this problem, our approach and our future plans to build a rash driving detector.Comment: 4 pages,7 figures, IBM-ICARE201