Characterisation of road bumps using smartphones

Introduction: Speed bumps are used as the main means of controlling vehicle speeds all over the world. It is not too infrequent, especially in the emerging economies, to have unmarked bumps that can be perilous for the passengers. Fortuitously, the roadways and mobile phone networks have grown simultaneously in emerging economies. This paper demonstrates the capability of smartphones placed inside the vehicles in characterisation of road bumps. The smart mobile phones have accelerometers and position sensors that can be useful for autonomous monitoring roads. This can empower the user community in monitoring of roads. However, the capability of the smartphone in discerning different types of speed bumps while travelling in heterogeneous vehicle types needs to be examined. Methods: A range of road vehicles is mathematically modelled as mass, spring, and damper systems. The mathematical model of the vehicle is excited with parameters analogous to some common speed bumps and its acceleration response is calculated. The accelerometer of a smartphone is validated by comparing it with high precision accelerometers. The acceleration response of the phone while passing over the corresponding road bumps, which was used in the model earlier, is recorded using an Android based application. The experiment is repeated for different classes of vehicles. Filters have been used to reduce noise in the signals. A time averaging technique has been employed to compress the collected data.Results and conclusions: The acceleration signals have been digitally processed to capture road bumps. The importance of using a mathematical model to understand the acceleration response of a vehicle has been established. Also, the use of pass filters to extract the signal of concern from the noisy data has been exhibited. The ability of the technique to discern different types of speed bumps while travelling in a variety of vehicle types has been demonstrated. This investigation demonstrates the potential to automatically monitor the condition of roadways obviating costly manual inspections. As smartphones are ubiquitous, the methodology has the potential to empower the user community in the maintenance of infrastructure

Kinematics of a smart variable caster mechanism for a vehicle steerable wheel

The lateral force of a tyre is a function of the sideslip and camber angles. The camber angle can provide a significant effect on the stability of a vehicle by increasing or adjusting the required lateral force to keep the vehicle on the road. To control the camber angle and hence, the lateral force of each tyre, we can use the caster angle of the wheel. We introduce a possible variable and controllable caster angle in order to adjust the camber angle when the sideslip angle cannot be changed. As long as the left and right wheels are steering together according to a kinematic condition, such as Ackerman, the sideslip angle of the inner wheel cannot be increased independently to alter the reduced lateral force because of weight transfer and reduction of the normal load F z A variable caster mechanism can adjust the caster angle of the wheels to achieve their top capacity and maximise the lateral force, when needed. Such a system would potentially increase the safety, stability, and maneuverability of the vehicles. Using the screw theory, this paper will examine the kinematics of a variable caster and present the required mathematical equation to calculate the camber angle as a function of suspension mechanism parameters and other relevant variables. Having a steered wheel about a tilted steering axis will change the position and orientation of the wheel with respect to the body of the car. This paper provides the required kinematics of such a suspension and extracts the equations in special practical situations. The analysis is for an ideal situation in which we substitute the tyre with its equivalent disc at the tyre plane. © 2012 Copyright Taylor and Francis Group, LLC

Artificial Intelligence and Internet of Things for autonomous vehicles

Artificial Intelligence (AI) is a machine intelligence tool providing enormous possibilities for smart industrial revolution. It facilitates gathering relevant data/information, identifying the alternatives, choosing among alternatives, taking some actions, making a decision, reviewing the decision, and predicting smartly. On the other hand, Internet of Things (IoT) is the axiom of industry 4.0 revolution, including a worldwide infrastructure for collecting and processing of the data/information from storage, actuation, sensing, advanced services and communication technologies. The combination of high-speed, resilient, low-latency connectivity, and technologies of AI and IoT will enable the transformation towards fully smart Autonomous Vehicle (AV) that illustrate the complementary between real world and digital knowledge for industry 4.0. The purpose of this book chapter is to examine how the latest approaches in AI and IoT can assist in the search for the AV. It has been shown that human errors are the source of 90% of automotive crashes, and the safest drivers drive ten times better than the average [1]. The automated vehicle safety is significant, and users are requiring 1000 times smaller acceptable risk level. Some of the incredible benefits of AVs are: (1) increasing vehicle safety, (2) reduction of accidents, (3) reduction of fuel consumption, (4) releasing of driver time and business opportunities, (5) new potential market opportunities, and (6) reduced emissions and dust particles. However, AVs must use large-scale data/information from their sensors and devices