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ADVANCED AERIAL MOBILITY MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

Advanced Aerial Mobility Market, By Type of Vehicle (Air Taxis, Cargo Drones, Emergency Medical Services (EMS) Drones, and Urban Air Mobility Vehicles), By Technology (Electric Vertical Takeoff and Landing (eVTOL), Hybrid VTOL, and Traditional Aviation), By Application (Passenger Transportation, Cargo Transportation, Surveillance and Monitoring, Emergency Services, and Agriculture), By Geography (North America, Latin America, Asia Pacific, Europe, Middle East, and Africa)

  • Published In : Sep 2024
  • Code : CMI7393
  • Pages :144
  • Formats :
      Excel and PDF
  • Industry : Aerospace and Defense
Market Challenge - Stringent regulations and safety concerns

Stringent regulations imposed by aviation regulatory bodies around the globe poses a significant challenge for the growth of global advanced aerial mobility market. Considering advanced aircrafts would carry passengers, stringent rules are being drafted to ensure highest level of safety. However, developing and complying with new regulations requires substantial investment of time and resources from manufacturers. Further, new technologies being used such as electric propulsion and autonomous operations raise safety concerns. All these new concepts would require rigorous testing and certification before being allowed for commercial passenger drone services. Establishing global standards for urban air mobility will be a lengthy process. Meanwhile, limited testing is allowed only under supervised conditions in select few regions. Overall, balancing strong safety measures with fast innovation and accessibility is a dilemma facing the industry.

Market Opportunity - Integration of advanced technologies such as AI and machine learning

The integration of advanced technologies such as artificial intelligence and machine learning offers huge potential for the global advanced aerial mobility market. AI-powered computer vision and sensor fusion can enable autonomous flight capabilities. Machine learning algorithms can help drones navigate crowded airspaces safely. It can also assist in functions such as obstacle avoidance, collision prevention, abnormal condition detection and emergency landing. The integration of AI brings opportunities to simulate complex scenarios for training autonomous flight systems. This reduces reliance on physical test flights and helps comply with stringent regulations. Data collected from drone operations can be leveraged for continuous improvement of AI models. The synergy between AI/ML and urban air mobility is paving the way for more autonomous, intelligent and safe advanced aircraft of the future.

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