An Intelligent Traction Control for Motorcycles

The appearance of anti-lock braking systems (ABS) and traction control systems (TCS) have been some of the most major developments in vehicle safety. These systems have been evolving since their origin, always keeping the same objective, by using increasingly sophisticated algorithms and complex brake and torque control architectures. The aim of this work is to develop and implement a new control model of a traction control system to be installed on a motorcycle, regulating the slip in traction and improving dynamic performance of two-wheeled vehicles. This paper presents a novel traction control algorithm based on the use of Artificial Neural Networks (ANN) and Fuzzy Logic. An ANN is used to estimate the optimal slip of the surface the vehicle is moving on. A fuzzy logic control block, which makes use of the optimal slip provided by the ANN, is developed to control the throttle position. Two control blocks have been tuned. The first control block has been tuned according to the experience of an expert operator. The second one has been optimized using Evolutionary Computation (EC). Simulation shows that the use of EC can improve the fuzzy logic based control algorithm, obtaining better results than those produced with the control tuned only by experience.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Detecting Semantic Parts on Partially Occluded Objects

In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the models should be inherently robust and adaptive to occlusions instead of fitting / learning the occlusion patterns in the training data. Our approach detects semantic parts by accumulating the confidence of local visual cues. Specifically, the method uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues. These cues are called visual concepts, which are derived by clustering the internal states of deep networks. We evaluate our voting scheme on the VehicleSemanticPart dataset with dense part annotations. We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions. Experiments show that our algorithm outperforms several competitors in semantic part detection when occlusions are present.Comment: Accepted to BMVC 2017 (13 pages, 3 figures

3D Pose Regression using Convolutional Neural Networks

3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. Most state-of-the-art approaches to 3D pose estimation solve this problem as a pose-classification problem in which the pose space is discretized into bins and a CNN classifier is used to predict a pose bin. We argue that the 3D pose space is continuous and propose to solve the pose estimation problem in a CNN regression framework with a suitable representation, data augmentation and loss function that captures the geometry of the pose space. Experiments on PASCAL3D+ show that the proposed 3D pose regression approach achieves competitive performance compared to the state-of-the-art

On-road Air Pollution Exposure to Cyclists in an Agent-Based Simulation Framework

Bicycle is not only a sustainable mode of transport but also health benefits of bicycling due to increased physical activities are well cited. However, in urban agglomerations, on-road air pollution exposure to cyclists/pedestrians is a matter of concern which is understudied. This study proposes an approach to calculate the on-road air pollution exposure for drivers of different vehicles in an agent-based simulation framework. In the proposed approach, the breathing rate of different drivers, penetration rate, vehicle-occupancy and background concentration are taken into consideration. The approach is applied to a real-world scenario of Patna, India where non-motorized modes are in abundance. A comparison of total inhaled mass per trip for drivers of different vehicles is made and it is found that cyclists are most exposed user group. An analysis for various background concentrations for different days of the year shows that the contribution of the background concentration has a major effect on the air pollution exposure level. The outcome is spatially analyzed to identify the locations of most affected user groups mapped to their home locations. Further, the on-road air pollution exposure of business-as-usual scenario is compared with a policy case and it is found that a dedicated bicycle track can increase the exposure per trip to cyclists by 40 %