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).

Today's premium vehicles can require up to 150 million lines of code distributed across over a hundred electronic control units, and utilize an ever-increasing number of sensors, cameras, radars, and lidar sensors. Lower-end cars have only slightly fewer. Three powerful trends—electrification, automation, and digitalization—are fundamentally changing customer expectations and forcing manufacturers to invest in software to meet them.

As driver assistance systems become automated and vehicles acquire autonomous driving capabilities, the demand for software also grows. The more sophisticated content consumers expect from their infotainment systems, the more digital content the vehicle must manage. And because such a vehicle, as part of the Internet of Things (IoT), transmits increasing amounts of data to and from the cloud, software is required to process and manage it.

There are two basic software development methods: the traditional waterfall model and a newer approach called agile, which is key to the automotive industry's transformation toward “software-defined vehicles.”

In the waterfall approach, software development proceeds through distinct, sequential phases. These phases include requirements definition, implementation, integration, and testing. The waterfall method has numerous drawbacks: it is not flexible enough to keep up with the pace of change in today's automotive industry; it does not emphasize close collaboration and rapid feedback from internal business teams and external partners; and it does not ensure testing occurs early enough in the project. The ability to test complex software systems is particularly important in the automotive industry, where engineers conduct advanced simulations of real-world driving conditions—software-in-the-loop, hardware-in-the-loop, and vehicle-in-the-loop—before a vehicle hits the road.

The agile approach represents a cultural and procedural shift from the linear and sequential waterfall approach. Agile is iterative, collaborative, and incorporates frequent feedback loops. Organizations using agile form small teams to address specific, high-priority business requirements. Teams often work in a fixed, relatively short iterative cycle, called a Sprint. Within this single iteration, they produce a complete and tested increment of a software product based on specific business needs, ready for stakeholder review.

Both waterfall and agile approaches can utilize the V-model for software development, sequentially progressing through the design, implementation, and testing phases. The difference is that the waterfall approach uses the V-model as a single-pass process throughout the project, while the agile approach may implement the V-model within each Sprint.

Software-defined vehicles (SDVs) open up enormous opportunities, allowing end customers to enjoy a wide range of cutting-edge safety, comfort, and convenience features. Well-organized cybersecurity management must go hand in hand with the development of SDVs. Software developers must ensure security in every area, regardless of the security of a specific application.

Cybersecurity is a relatively new concept in the automotive industry [1]. While automakers began introducing electronically controlled steering and braking systems into their vehicles, the likelihood of threats increased, connectivity opened the door to significantly greater risks. Direct vehicle connections to the internet are cited as a source of cyber threats, but indirect connections, such as those to a mobile phone connected via USB or Bluetooth, are often overlooked. Even a vehicle that appears to have no connectivity at all can be equipped with a wireless tire pressure monitoring system or on-board diagnostic module that allows access to vehicle information.

Here are some key areas related to this topic:

• Secure updates: To ensure consumers have access to the most efficient features, today's vehicles download software updates wirelessly from the cloud, so these updates must be secure. Public key infrastructure (PKI) is a mechanism that allows manufacturers to digitally sign software so that the receiving system can verify its authenticity. Using a secret digital key, the manufacturer encrypts the software before releasing it. When the vehicle downloads the software, it uses another, publicly available key to verify the content. A complex algorithm ensures that only content signed with the secret key can be verified with the public key.
• Safe system boot: Manufacturers must also ensure secure system boot. Once the vehicle is started, the system must verify the authenticity and integrity of the software. This means the system must ensure that the code was created by the manufacturer and not by an attacker.
• Secure vehicle network: As vehicles become increasingly complex and software-defined, many applications running on them will use the same processors and networks to transmit data between different processing nodes. For example, some infotainment applications might require vehicle speed and navigation data, while other applications might need battery management information. Having an in-vehicle network and connecting to cloud networks via mobile network and Wi-Fi requires vehicles to secure these connections on multiple layers. The lowest layer is Media Access Control Security (MACsec), which establishes a bidirectional encrypted connection between two directly communicating devices. MACsec can operate extremely quickly, encrypting and decrypting information at wire speeds using specialized hardware. The next higher layer is Internet Protocol Security (IPsec), which operates at the network layer to authenticate and encrypt data packets between network nodes with IP addresses. Using IPsec can help protect data flowing across the network—through a router, to the cloud, and beyond—not just on the physical link between two points. Moving up the stack, manufacturers can use Transport Layer Security (TLS). This protocol operates at the network layer, where processes communicate without being bound to IP addresses, making the security mechanism more flexible. TLS is now widely used in internet communications, and vehicles should use it when connecting to the cloud.

As autonomous driving becomes more common, implementing even higher levels of message encryption may become increasingly important. For example, a user could send a message to a vehicle requesting it be picked up from a specific address. This message would be cryptographically signed and delivered securely. New protocols can help with this, even involving multiple clouds if necessary. Furthermore, as automotive companies begin to hire more developers to build various software functions, ensuring no interference between applications becomes crucial. This is achieved by using hypervisors, containers, and other technologies to decouple software, even on shared hardware.

Recent advances in vehicle radar technology will soon lead to a fundamental increase in radar capabilities, significantly enhancing the radar-centric approach to advanced driver assistance systems (ADAS).

There are two primary ways to improve ADAS system perception. We can either improve how the system interprets sensor data using machine learning, or improve the quality and accuracy of real-world sensor data. Combining these approaches creates a synergistic effect, creating a reliable model of the environment that vehicles can use to make intelligent driving decisions. While machine learning is constantly evolving, so is radar sensor technology. One specific technology is the use of 3D air-waveguide technology in radar antennas to capture more precise signals and increase range. In automotive radar systems, 3D air-waveguide antennas help efficiently scan the surrounding area with radar signals and receive weak echoes from the surrounding environment with low loss. By reducing losses in the transmitted and received signal, air-waveguide antennas enable the use of a more sensitive sensor while maintaining the same small physical radar footprint.

Radars with advanced 3D air-waveguide antenna technology could support a high-resolution perception mode, enabling self-parking. Early implementations of self-parking systems rely on ultrasonic sensors to measure the width of parking spaces. This often means the vehicle must drive past the space to determine if it is the right size before backing into it. With ambient perception software that utilizes improved radar, the vehicle would be able to determine the space's dimensions before driving past it, allowing it to park directly in it.

By utilizing advanced antenna technology, high-speed data transmission support, and enhanced software, Aptiv’s seventh-generation radar family provides an excellent foundation for building the next generation of automated driving vehicles.

At the same time, in the case of high-speed NOA radars, the detection range of the traditional radar is only 210 meters when traveling at high speed, while the detection range of the precise 4D radar reaches 280 meters (the parameters of some manufacturers even exceed 300 meters), which allows for earlier identification of targets, thereby solving the problem of the rigid requirement for forward perception.

Currently, brands such as Ideal, Changan, and Weilai are leading the widespread adoption of 4D radars. Among them, all Weilai NT3 platform models will be equipped with 4D radars as standard, with a maximum detection range of up to 370 meters. Furthermore, the three millimeter-wave distributed radar arrays in the Huawei Zunjie S800 can also utilize 4D technology.

Moreover, according to Huawei, the distributed architecture further improves performance. Its detection capabilities surpass those of existing 4D millimeter-wave radars, its high-confidence detection range in rainy and foggy conditions has been increased by 60%, and the detection latency of frontal and side targets has been reduced by 40%.