Cairo ranks as one of the most polluted cities and recently traffic congestion has been a major problem in its streets. One of the viable solutions is reducing vehicles and increasing the use of bikes. However, cyclists in Cairo are facing many challenges, which makes the use of the bike slightly inconvenient in this case. In order to reduce the challenges and ensure safe navigation, this paper provides a multi-criteria route planner developed specifically for the streets of Cairo. The system provides the best possible path which maintains the comfort, health, and safety of the rider. The system is able to operate independent of information regarding bike lanes and traffic rules. Furthermore, due to the significant impact of the weather on cycling decisions, real-time weather information was added as a factor within the proposed routing function. Real-life testing of the best routes and their alternatives was conducted to assess the applicability of the system. The proposed approach in this paper can be applied in any location, where the required online information is provided without any need for specialized infrastructure.

Research interest in autonomous systems for vehicles has received increasing attention in the past few years. Multiple test vehicles and platforms reached advanced levels of autonomy. Focusing on modules that enable advanced driver assistant systems as well as modules within automated stacks, there has been a small amount of research based on smart bike platforms. Given this case, this paper proposes a module for rider alertness. The module takes into account the rider’s level of alertness in real-time. This is essential as cyclists might sometimes face biological traumas that affect their alertness and decision-making ability, which they need to be aware of. Moreover, if any signs of drowsiness are shown facially, they are considered in the decision-making process and the system is capable to override the whole riding process if necessary. This prototype system in turn increases the safety of the rider and can reduce accidents.

The increasing number of vulnerable road users is causing an increase in the number of accidents on the road. In order to control the accidents and increase the safety of cyclists and other road users, multiple solutions were explored. These solutions include improving smart modules on vehicles, smart bikes and infrastructure. Although smart bikes have been in rapid production as a modern alternative for regular bikes, the development of the safety modules is still lagging behind, especially in real-time communication systems. These systems can help in reducing the severity of collisions or even preventing them altogether. Given the current state of research in the field of communication, this paper focuses on developing a secure low-power multiprotocol communication system to connect smart bikes with vehicles, people, and all nearby entities in real-time.

Recently, autonomous systems have been in rapid development. These systems are composed of various modules that enable automated navigation. The main generic modules consist of data acquisition, perceiving the environment, finding the optimal path and controlling the actuators. A core module of the automated vehicles is estimating where the vehicle is with respect to the path to be traversed, namely, vehicle localization. Localization has been applied through the past few years based on various sensors such as LiDARs, Cameras and GPS. Each sensor has its own strengths and weaknesses. For instance, Camera and LiDAR-Based traversed path estimation Odometry can function within GPS-denied environments. On the other hand, localization based on GPS can be used in environments where Cameras and LiDARs can fail to extract useful information, such as in a desert. For the case of automated vehicles, there is access to high power computers on board, which can help run complex and computationally expensive algorithms. However, for smaller platforms such as smart bikes, they are computationally limited, which can be a challenge especially for Visual Odometry Based algorithms. Given that there have been few researches exploring Monocular Visual Odometry (MVO) algorithms that are computationally efficient, this paper proposes the use of and enhancing a MVO module to estimate the path traversed by a smart bike platform in real-life. The obtained scores were compared to the output of the tested algorithm on a consumer grade PC to explore the trade-off between the gained speed up and reduced accuracy, the to validate the results.

The accuracy of pose estimation from feature-based Visual Odometry (VO) algorithms is affected by several factors such as lighting conditions and outliers in the matched features. In this paper, a generic image processing pipeline is proposed to enhance the accuracy and robustness of feature-based VO algorithms. The pipeline consists of three stages, each addressing a problem that affects the performance of VO algorithms. The first stage tackles the lighting condition problem, where a filter called Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to the images to overcome changes in lighting in the environment. The second stage uses the Suppression via Square Covering (SSC) algorithm to ensure the features are distributed properly over the images. The last stage proposes a novel outliers rejection approach called the Angle-based Outlier Rejection (AOR) algorithm to remove the outliers generated in the feature matching process. The proposed pipeline is generic and modular and can be integrated with any type of feature-based VO (monocular, RGB-D, or stereo). The efficiency of the proposed pipeline is validated using sequences from KITTI (for stereo VO) and TUM (for RGB-D VO) datasets, as well as experimental sequences using an omnidirectional mobile robot (for monocular VO). The obtained results showed the performance gained by enhancing the accuracy and robustness of the VO algorithms without compromising on the computational cost using the proposed pipeline. The results are substantially better as opposed to not using the pipeline.

The effect of Greenhouse Gases on the environment has been increasing rapidly. In attempts to reduce pollution and the consequent Greenhouse effect, electric vehicles were introduced. For short travels, cheaper and greener alternatives such as electric smart bikes are becoming a hype. E-bikes are convenient, however, improving the modules available to better suit Smart E-bikes can make it more convenient for the users. The State of Charge (SOC) of the battery is an example. An accurate estimation of the SOC in an E-Bike is important, however, it cannot know if the current path selected by the rider will be covered fully by the remaining battery power. To make the available E-Bike modules smarter, battery estimation-path traversal in this case, this paper proposes a battery energy estimation system that notifies the rider if their chosen route is within the range that the current battery state can handle. This safety feature insures that users will always have power when traversing an approved route by the estimation system. In addition, the system is tested on a real-life prototype smart autonomous E-Bike to validate the results of the proposed approach.