Domain-Specific Challenges in Autonomy

[rczyba][✓ rczyba, 2025-10-20]

Autonomous technologies and robotics are redefining possibilities, improving efficiency and safety across sectors. Advanced applications such as self-driving vehicles, crop and harvesting robots rely on precise GNSS/GPS positioning and require centimeter-level accuracy to function properly. As the domain of autonomous applications expands, the ability to use reliable, real-time GNSS/GPS correction services becomes not only useful, but essential. Mapping and localization algorithms, as well as sensor models, enable vehicle orientation even in unfamiliar environments. Route planning and optimization algorithms, as well as obstacle avoidance algorithms, enable vehicles to reach their destinations independently. The development of autonomous driving technology also involves the introduction of systems that enable communication between self-driving cars, as well as between the AVs and their surroundings. Autonomous driving technology is constantly evolving, and among the greatest challenges associated with developing fully functional self-driving cars is the dependence of individual sensors' performance on weather conditions.

A significant challenge is how to navigate autonomously by unmanned vehicles in environments with limited or no access to localization data. Autonomous navigation without GNSS is a complex and rapidly evolving technology area that has the potential to revolutionize many industries and applications. Key technologies and methods for navigation that lack GNSS information include inertial navigation systems (INS), vision-based localization, Lidar, and indoor localization systems. Promising results are also provided by SLAM technology, which is used to simultaneously determine the vehicle's position (location) and build a map of the environment in which it moves. Each of the technologies mentioned above has its advantages and disadvantages, but none of them provides a complete overview of the current state of the surrounding world. Although autonomous navigation technology without GNSS has many advantages, it also encounters challenges. These include, among others, difficulties in accurate measurement in an unknown or changing environment and problems with sensor calibration, which can lead to navigation errors.

In recent decades, much research and technology has been developed for various autonomous systems, including airborne, ground-based, and naval systems (see Figure 1). Much of this technology has already reached maturity and can be implemented on unmanned platforms, while others are still in the research and development phase.

Domain-specific challenges in the autonomy of vehicles include several technical, safety, regulatory, and ethical issues unique to different operational environments. Key challenges are:

• Sensor Limitations and Perception
  • Accurate object detection in diverse weather conditions (rain, fog, snow).
  • Distinguishing between road users like pedestrians, cyclists, and animals.
  • Handling sensor occlusions and blind spots.
• Complex and Dynamic Environments
  • Navigating complex urban settings with unpredictable human behavior.
  • Managing construction zones, roadworks, and unexpected obstacles.
  • Adapting to high traffic density and erratic driver behaviors.
• Localization and Mapping
  • Achieving precise real-time localization in GPS-denied environments (tunnels, urban canyons).
  • Maintaining up-to-date high-definition maps amid road changes.
• Decision-Making and Planning
  • Ensuring safe, compliant, and contextually appropriate decisions.
  • Handling edge cases like jaywalking pedestrians or unusual vehicle maneuvers.
• Communication and Cybersecurity
  • Secure vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications.
  • Protecting against hacking or malicious cyber-attacks.
• Regulatory and Legal Challenges
  • Navigating diverse legal frameworks across regions.
  • Defining liability in accidents involving autonomous vehicles.
  • Achieving standardization and interoperability.
• Ethical Considerations
  • Decision-making in unavoidable accident scenarios.
  • Privacy concerns related to data collection and sharing.
• Infrastructure and Standardization
  • Lack of uniform infrastructure to support vehicle-to-infrastructure communication.
  • Variability in road signage, markings, and infrastructure standards.
• Testing, Validation, and Certification
  • Developing comprehensive testing protocols for safety assurance.
  • Validating autonomous systems in all possible scenarios.
• Human Factors and User Acceptance
  • Ensuring passenger trust and understanding of autonomous system limits.
  • Managing transition of control between human and automation.

Solving these domain-specific challenges is crucial for the safe and reliable deployment of autonomous vehicles in the various operational scenarios of our lives. In the following subchapters, specific challenges are determined taking into account the vehicle type and its operating environment.

Building public trust in autonomous vehicles will be a huge challenge. In 2025, an online preference survey collected 235 responses from across Europe ^[2]. Respondents were asked whether they would feel comfortable traveling in an autonomous car. According to over half of the respondents, driving a driverless autonomous car would make them feel uncomfortable. Distrust of machines is primarily fueled by the prospect of losing control over one's fate, which is one of the fundamental philosophical issues in the context of artificial intelligence development. Therefore, it seems crucial for sociologists and psychologists to thoroughly examine this fear and develop a plan to educate the public. Furthermore, placing excessive reliance on automated systems delays drivers' reaction times in crisis situations and compromises their willingness to take manual control. Increased trust also leads to longer driver reaction times to roadside warnings.

Another considered advantage of introducing autonomous vehicles in public transport would be the reduction of fatal accidents. According to statistics, driver error contributes to 75–90% of all road accidents. Eliminating driver error could significantly reduce fatalities among drivers and passengers. However, critics of this approach point out that automation can only correct some human errors, not eliminate them entirely. However, forecasts regarding increasing access to this technology are optimistic. According to the EU Transport Commissioner, by 2030, roads in member states will be shared by cars with conditional automation and standard vehicles. The prospect of full automation is another 10–15 years away. The research performed using the autonomous Blees bus ^[3] constructed in Gliwice, Poland, confirms the results of the survey and allows us to look optimistically into the future.

Software-based vehicle (SV) is a term used to describe, among other things, cars whose parameters and functions are controlled by software. This is the result of the car's ongoing evolution from a purely mechanical product to a software-based electronic device (see Figure 3).