Inverse Perspective Mapping Roll Angle Estimation for Motorcycles

International audienceThis paper presents an image-based approach to estimate the motorcycle roll angle. The algorithm estimates directly the absolute roll to the road plane by means of a basic monocular camera. This means that the estimated roll angle is not affected by the road bank which is often a problem for vehicle observation and control purposes. For each captured image, the algorithm uses a numeric roll loop based on some simple knowledge of the road geometry. For each iteration, a bird-eye-view of the road is generated with the inverse perspective mapping technique. Then, a road marker filter associated with the well-known clothoid model are used respectively to track the road separation lanes and approximate them with mathematical functions. Finally, the algorithm computes two distinct areas between the two-road separation lanes. Its performances are tested by means of the motorcycle simulator BikeSim. This approach is very promising since it does not require any vehicle or tire model and is free of restrictive assumptions on the dynamics

An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio

Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed