Current Trends in Automotive Lightweighting Strategies and Materials

The automotive lightweighting trends, being driven by sustainability, cost, and performance, that create the enormous demand for modern lightweight materials and design concepts, are assessed as a part of the circular economy solutions in modern mobility and transportation. The current strategies that aim beyond the basic weight reduction and cover also the structural efficiency as well as the economic and environmental impact are explained with an essence of guidelines for materials selection with an eco-friendly approach, substitution rules, and a paradigm of the multi-material design. Particular attention is paid to the metallic alloys sector and progress in global R&D activities that cover the “lightweight steel”, conventional aluminum, and magnesium alloys, together with well-established technologies of components manufacturing and future-oriented solutions, and with both adjusting to a transition from internal combustion engines to electric vehicles. Moreover, opportunities and challenges that the lightweighting creates are discussed with strategies of achieving its goals through structural engineering, including the metal-matrix composites, laminates, sandwich structures, and bionic-inspired archetypes. The profound role of the aerospace and car-racing industries is emphasized as the key drivers of lightweighting in mainstream automotive industry.

Racing in the Dark: Exploring Accessible Virtual Reality by Developing a Racing Game for People who are Blind

Video games are popular leisure and social activities; however, they often present accessibility issues for the community of gamers who are blind. While gaming technology has evolved, people who are blind still have limited access to commercially available accessible video games. Current accessible games tend to focus on auditory information to play, and none are available in virtual reality (VR). Accessible, fast-paced games have the development challenge of providing players useful information in time to make split-second decisions. We developed Racing in the Dark, a fast-paced, accessible VR racing game for the Oculus Quest, exploring commercial VR interfaces to provide this critical information. By leveraging the Quest’s built-in haptic, tracking, auditory, and voice systems, we provide players with a non-visual car racing experience. This work aims to offer insight into the process of developing accessible VR games for gamers who are blind through the implementation of Racing in the Dark.

Applying a Deep Q Network for OpenAIs Car Racing Game

The applications of Deep Q-Networks are seen throughout the field of reinforcement learning, a large subsect of machine learning. Using a classic environment from OpenAI, CarRacing-v0, a 2D car racing environment, alongside a custom based modification of the environment, a DQN, Deep Q-Network, was created to solve both the classic and custom environments. The environments are tested using custom made CNN architectures and applying transfer learning from Resnet18. While DQNs were state of the art years ago, using it for CarRacing-v0 appears somewhat unappealing and not as effective as other reinforcement learning techniques. Overall, while the model did train and the agent learned various parts of the environment, attempting to reach the reward threshold for the environment with this reinforcement learning technique seems problematic and difficult as other techniques would be more useful.