In this work, we propose a data-driven design pipeline for quick design exploration of performance and appearance guided alternatives for vehicle design. At the heart of our system is a machine learning-based generative design method to provide users with a set of diverse optimal design alternatives and an interactive design technique to induce users' preference into the design exploration. The generative design method is structure on two search process, qualitative and quantitative. To avoid the curse of dimensionality, the qualitative search process first builds up a lower-dimensional representation of a given design space, which is then explored using the unsupervised k-means clustering to synthesise a representative set of user-preferred designs. The quantitative search process explores the design space to find an optimal design in terms of performance criterion such as drag coefficient. To reduce the computational complexity, instead of evaluating drag via Computational Fluid Dynamics simulations, a surrogate model is developed to predict the drag coefficients. The designs generated after the generative design step are presented to the user at the interactive step, where potential regions of the design space are identified around the user-selected designs. Afterwards, a new design space is generated by removing the nonpreferred regions, which helps to focus the computational efforts on exploring the user preferred regions of the design space for a design tailored to the user's requirements. We demonstrated the performance of the proposed approach on a two-dimensional side silhouette of a sport-utility vehicle.

Road accidents are a common occurrence throughout the world. The development of efficient electric vehicle (EVs) with high levels of safety is one of today’s biggest challenges. In this article, a novel modular bike helmet based on an RF transmitter and a helmet side based on Arduino Uno, an accelerometer and an LED array that can relay information to approaching vehicles has been proposed. Current motorbike helmets are a form of passive protective gear that only serve the purpose of avoiding fatal damage to the skull. The proposed helmet will add to the current functionality of a helmet by making it smarter, giving it a means of preventing an accident. The proposed helmet will broadcast information about the biker’s movements, such as acceleration and deceleration, to the approaching vehicles. This information has never been broadcasted to approaching vehicle before. Additionally, common turn and stop signals will be broadcasted, allowing the driver of any approaching vehicle to take informed decision that can ensure both their safety and that of the biker.

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