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Passenger drones are currently being developed in some countries to solve the purpose of point-to-point commute. Even though passenger drones have been developed and are developing in some countries, the studies done on designing the interiors have focused on the western audience. Therefore, an in-depth study is required to create cabin interiors for passenger drones, which can suit the country's varied aspects like India. This paper focuses on the user requirements based on a survey of the color, form, and possible future inflight activities conducted for interior design and development for passenger drones. The methodology used was a survey among the potential users. To understand the interaction, color, and form, and map the needs and activities of the passengers in a fully autonomous passenger drone, literature review on proposed activities, future trends of color and form forecast of the transportation industry was studied and used as an input for questionnaire design. An online survey was conducted to gain insights on capturing the futuristic travel experience and activities when inside a fully autonomous cabin. Questions related to the future prediction of automotive trends and possible activities within the cabin were asked. Quantitative data analysis was done after converting the data to a pivot chart. The paper explores and discusses an overview of the future form and color trends and mapping future possible inflight activities in a fully autonomous passenger drone. It also discusses the various possibilities that can be explored to design and develop a passenger drone interior cabin. [ABSTRACT FROM AUTHOR]

Autonomous driving is the future of the automotive industry across the globe. Many challenges must be resolved in designing and developing successful Autonomous Driving Systems (ADS), especially in developing countries with poor road infrastructure. Detecting obstacles in lanes is essential for robust ADS to decide whether to change lanes, slow down, or even stop. Developing countries like India present challenges, including poor or no lane line demarcation, poor traffic management, and diverse driving behaviors. That brings many challenges to developing robust lane-keeping or lane-changing decision systems in an ADS. Publicly available driving datasets have an inherent bias towards the road infrastructure of developed countries and hence cannot cater to the needs of the Indian requirements. That makes the existing obstacle-lane segment detection models unsuitable for road infrastructures in developing countries. Due to these factors, the rate of advancement of ADS and Advanced Driver Assistance Systems (ADAS) in developing countries is meager compared to other developed countries. This makes it even more critical for future research to focus on these developing countries. This paper proposes a deep-learning-based novel decision-making network named LaneScanNET to assist the ADS in lane-changing or lane-keeping decision-making. The proposed system assists ADS in detecting obstacles, localization of the Ego Vehicle (EGV) on roads, and estimating lane status in its Field of View (FOV). The proposed LaneScanNET uses a parallel pipeline with an Obstacle Detection Network (ODN) and a Lane Detection Network (LDN) to simultaneously process the incoming image frames for detecting obstacles and segmenting lane lines, respectively. Further, the Obstacle-Lane Fusion Network (OLFN) fuses these results to predict the status of the obstacle lane in the FOV of autonomous vehicles. Vellore Institute of Technology's (VIT's) real-time driving dataset on indigenous roads has been collected to train the proposed LaneScanNET with 2464 obstacle-lane images obtained by driving over 60 km. The dataset generated for obstacle detection and its corresponding lane detection has also been made publicly available to promote research work for these developing countries. The proposed system outperforms all existing networks on Indian roads with an accuracy of 75.28% in obstacle detection and 91.36% in lane detection. Additionally, the proposed system can analyze the lane status with an accuracy of 92.54%. The proposed network performs exceptionally well in unforeseen circumstances like shadows, fog, dust, and occlusions making LaneScanNET a robust network that can be an add-on for ADS to make lane-keeping or lane-changing decisions. The LaneScanNET can be integrated into real-time vehicles to assist drivers or ADS. [ABSTRACT FROM AUTHOR]

The rise of autonomous vehicles (AV) will revolutionize the transportation industry and it will transform the society itself, much like the nineteenth century shift from horse-carriages to automobiles. The automobile industry as well as governments/authorities all around the world are trying to create vehicles and road traffic infrastructures to support this futuristic public/private transportation technology. In our paper we are putting forward a feasible method of implementing the autonomous vehicular technology in a developing country like India as a public transport system in itself or as a feeder system to public transport systems like metro by using the concepts of safe state and kill switch. [ABSTRACT FROM AUTHOR]