Car-following and Lane-changing are two fundamental tasks during driving. While many car-following models can be applied, relatively, only a few lane-changing models have been developed. Classical lane-changing models mainly focus on drivers’ lane selection and gap acceptance behaviors, but very limited research has paid attention to formulating detailed lane-changing trajectories. This research aims to fill the gap by proposing a lane-changing trajectory model, which is built directly from drivers’ vision view, to model detailed lane-changing trajectories. A large amount of data of reference angles, defined as the angle changes between the drivers’ vision angle and left or right lane line, were first extracted from the videos recorded by the vehicle traveling data recorders (VTDRs) installed in 11 taxies. A comprehensive data analysis indicates that same drivers show similarity of their daily lane-changing habit but with variety, and different drivers’ lane-change trajectory data show different lane-change “personality” including aggressive or non-aggressive behaviors. Based on these findings, this paper then proposed a hyperbolic tangent lane-change trajectory model to describe drivers’ detailed lane-change trajectories. The model is verified using both real data and simulation. The results show the proposed lane-change trajectory model can successfully describe drivers’ lane-changing trajectories. More importantly, some parameters in the model are directly associated to drivers’ driving characteristics during lane-change. With this unique feature, the proposed model can generate driver-specific lane-change trajectories. Such improvement could contribute to the future development of Advanced Driver Assistance Systems (ADAS).

A lane-change trajectory model from drivers’ vision view

Many CNN-based segmentation methods have been applied in lane marking detection recently and gain excellent success for a strong ability in modeling semantic information. Although the accuracy of lane line prediction is getting better and better, lane markings' localization ability is relatively weak, especially when the lane marking point is remote. Traditional lane detection methods usually utilize highly specialized handcrafted features and carefully designed postprocessing to detect the lanes. However, these methods are based on strong assumptions and, thus, are prone to scalability. In this work, we propose a novel multitask method that: 1) integrates the ability to model semantic information of CNN and the strong localization ability provided by handcrafted features and 2) predicts the position of vanishing line. A novel lane fitting method based on vanishing line prediction is also proposed for sharp curves and nonflat road in this article. By integrating segmentation, specialized handcrafted features, and fitting, the accuracy of location and the convergence speed of networks are improved. Extensive experimental results on four-lane marking detection data sets show that our method achieves state-of-the-art performance. 

Multitask Attention Network for Lane Detection and Fitting

Fast and accurate visual scene understanding in autonomous vehicles is necessary but still very challenging. An autonomous vehicle must be taught to read the road like a human driver for better controlling the vehicle, so it is important to efficiently detect the road area and road markings. In this paper, we mainly focus on the vanishing point detection and its application in inverse perspective mapping (IPM) for road marking understanding. We first propose a fast and accurate vanishing point detection method for various types of roads, by adopting and improving Weber local descriptor to obtain salient representative texture and orientation information of the road area, and then voting for the dominant vanishing point with a simple line-voting scheme. Experimental results demonstrate that the proposed vanishing point detection approach gains a better performance than some state-of-the-art methods in terms of accuracy and computation time. Furthermore, we introduce the detected vanishing point into the IPM algorithm in the structured road environment, since some important calibration parameters can be automatically calculated by the vanishing point, especially on the rough road. Experiments also show that our proposed vanishing point-based IPM method is adaptive and accurate, which is conducive to the subsequent road marking detection and recognition.

Fast and Accurate Vanishing Point Detection and Its Application in Inverse Perspective Mapping of Structured Road

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird'seye-view images contain sharper features (e.g, road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford Robot-Car Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Perspective is a fundamental structure that is found to some extent in most images that reflect 3D structure. It is thought to be an important factor in the human visual system for obtaining understanding and extracting semantics from visual material. This paper describes a method of detecting vanishing points in images that does not require prior assumptions about the image being analysed. It enables 3D information to be inferred from 2D images. The approach is derived from earlier work on visual attention that identifies salient regions and translational symmetries.

Attention-Based Vanishing Point Detection.

Vision-based road signs detection and recognition has been widely used in intelligent robotics and automotive autonomous driving technology. Currently, one-time calibration of inverse perspective mapping (IPM) parameters is employed to eliminate the effect of perspective mapping, but it is not robust to the uphill and downhill road. We propose an automatic inverse perspective mapping method based on vanishing point, which is adaptive to the uphill and downhill road even with slight rotation of the main road direction. The proposed algorithm is composed of the following three steps: detecting the vanishing point, calculating the pitch and yaw angles and adopting inverse perspective mapping to obtain the “bird's eye view” image. Experimental results show that the adaptability of our inverse perspective mapping framework is comparable to existing state-of-the-art methods, which is conducive to the subsequent detection and recognition of road signs.

Robust inverse perspective mapping based on vanishing point

Most existing approaches detect the vanishing point by voting the local dominant texture orientation at each pixel. However, when it is hard to distinguish natural road clues (items for finding the vanishing point) from background noises (e.g. stones, grasses) in complex scenes, they may suffer deteriorated accuracy and efficiency. In this paper, the authors introduce a novel vanishing point detection algorithm with the proposed Weber Orientation Descriptor (WOD). The authors first employ the differential excitation component of WOD to extract reliable road clue regions from a complex background, and then adopt the orientation component of WOD and the authors proposed line-voting scheme (LVS) to locate the vanishing point. Experimental results on the benchmark dataset reveal a step forward in detection performance against the state-of-the-art vanishing point detection methods.

Fast and accurate vanishing point detection in complex scenes

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Robust inverse perspective mapping based on vanishing point

Fast and accurate vanishing point detection in complex scenes