Towards End-to-End Lane Detection: an Instance Segmentation Approach

Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed "bird's-eye view" transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results

End-to-end Lane Detection through Differentiable Least-Squares Fitting

Lane detection is typically tackled with a two-step pipeline in which a segmentation mask of the lane markings is predicted first, and a lane line model (like a parabola or spline) is fitted to the post-processed mask next. The problem with such a two-step approach is that the parameters of the network are not optimized for the true task of interest (estimating the lane curvature parameters) but for a proxy task (segmenting the lane markings), resulting in sub-optimal performance. In this work, we propose a method to train a lane detector in an end-to-end manner, directly regressing the lane parameters. The architecture consists of two components: a deep network that predicts a segmentation-like weight map for each lane line, and a differentiable least-squares fitting module that returns for each map the parameters of the best-fitting curve in the weighted least-squares sense. These parameters can subsequently be supervised with a loss function of choice. Our method relies on the observation that it is possible to backpropagate through a least-squares fitting procedure. This leads to an end-to-end method where the features are optimized for the true task of interest: the network implicitly learns to generate features that prevent instabilities during the model fitting step, as opposed to two-step pipelines that need to handle outliers with heuristics. Additionally, the system is not just a black box but offers a degree of interpretability because the intermediately generated segmentation-like weight maps can be inspected and visualized. Code and a video is available at github.com/wvangansbeke/LaneDetection_End2End.Comment: Accepted at ICCVW 2019 (CVRSUAD-Road Scene Understanding and Autonomous Driving

Activity Participation and Perceptions on Informal Public Transport and Bus Rapid Transit in Dar es Salaam

This paper seeks to understand participation in out-of-home activities by inhabitants in Dar es Salaam, and their perceptions toward informal public transport (IPT) and bus rapid transit (BRT) in supporting these activities. Without fixed schedules, IPT (e.g., minibuses, motorcycles, and tricycles) is used as a means of transport for different trips. However, IPT is burdened by poor roads, traffic congestion, and high transport demand. Many developing cities are seeking to replace IPT with formal BRT lines. However, little is known in relation to the ability of IPT and BRT to support out-of-home activity participation of the inhabitants. This paper reports on a study in Dar es Salaam exploring the relative contribution of each type of service. The study took place before the opening of BRT, and encompasses focus group discussions, participatory geographical information systems, and questionnaires carried out in two study zones: one close to a BRT corridor and the other in a peri-urban location. The findings show that IPT was used to support participation in daily activities like work, education, shopping, and social matters; and was perceived to be flexible in providing access to both high and low density unplanned settlements. The BRT was viewed to benefit specific groups of people, especially individuals working in permanent offices in and around the city center, particularly professional workers. This paper sheds light on how the two systems were perceived by the local people and can inform policy makers about possible improvements in public transport systems to support activity participation of their inhabitants

TRR 2650

Persons with intellectual disabilities (PwIDs) often aspire to more social inclusion by engaging in more community activities but encounter social barriers when traveling independently. Therefore, PwIDs are often accompanied by family members, friends, or volunteers. In order to both support the independent outdoor mobility of PwIDs themselves and reduce the caregivers' burden, the geographic information system-based application "Viamigo" was developed (www.viamigo.be), which allows a personal coach to monitor an individual in real time from a distance. The goal is to teach PwIDs a known individual route that they can accomplish independently while being monitored by a personal coach, caregiver, family member, or friend who is taking care of the individual making the trip. Viamigo determines the location of the user and compares this in time and space within a predetermined range and automatically sends notifications to the coach in case the user deviates from the route, travels at an incorrect speed, or enters or leaves a safe or dangerous zone, among other factors. Besides this on-route functionality, Viamigo also allows the creation of geofences around destinations (to monitor whether the user stays within a predefined zone) and emergency tracking. The initial results are promising: PwIDs successfully used Viamigo for a heterogeneous set of trips performed by several travel modes (bus, cycling, and walking) for several activity purposes (both daily recurrent trips to the day center and trips for shopping, social, and recreational purposes) and for different distances