autonomous driving Archives

10 July, 2025 . 8 mins read

The State of Autonomous Driving in 2025

Building on our previous post examining the industry’s transition from SAE Level 2 to Level 3 autonomy, this article revisits the topic in light of regulatory and commercial developments around autonomous driving. Our earlier analysis found that the slow progession toward Level 3 autonomy has been driven more by regulatory uncertainty than by technological limitations. Due to ongoing legal bottlenecks, we observed that OEMs introduced Level 2+ systems but remain hesitant to classify them as Level 3, primarily because of unresolved concerns around legal responsibility and risk management.

Since then, the regulatory and commercial landscape for autonomous driving has continued to evolve. This article highlights how recent policy shifts have accelerated Level 3+ deployment and testing efforts, while also examining the growing importance of open-source software in enabling software-defined vehicle (SDV) development. As SDVs grow more complex — both technically and in terms of regulatory oversight — it has become essential for OEMs and Tier 1 suppliers to stay aligned with ongoing developments and adapt their cybersecurity practices accordingly.

Bridging Regulation and Deployment in Autonomous Driving

As commercial interest in Level 3+ autonomy grows, regulatory developments have played a pivotal role in shaping a more stable legal environment for innovation. Both globally and regionally, recent updates have provided clearer guidelines for deployment, liability, and compliance. Among the most impactful are the ongoing amendment series to UNECE Regulation No. 157 on Automated Lane Keeping Systems (ALKS) and the introduction of UNECE Regulation No. 171 on Driver Control Assistance Systems (DCAS).

Global Regulatory Progress in Autonomous Driving

The UNECE Regulation No.157 on Automated Lane Keeping Systems (ALKS) was first adopted by the World Forum for Harmonization of Vehicle Regulations (WP.29) in January 2021 to govern SAE Level 3 conditional automation. Since enforcement began in January 2023, successive amendments introduced from 2022 onward have significantly clarified the operational behavior, system safety, and failsafe protocols required for real-world applications.

WP.29/2022/59/Rev.1 Amendment: Speed limit raised from 60km/h to 130km/h, Automated lane changes allowed

WP.29/2024/145 Supplement: Ensure ALKS system resilience in electromagnetic environments

WP.29/2025/64 Supplement: Clearer rules for lane-change behavior and heavy vehicle restrictions

In parallel with ALKS, UNECE Regulation No.171 on Driver Control Assistance Systems (DCAS) established safety requirements for SAE Level 2 driver assistance features, including lane keeping and traffic jam assist. The regulation emphasizes stricter standards for driver engagement, monitoring systems and interface transparency. Together, these two frameworks — covering foundational technologies like ALKS and DCAS — have strengthened the regulatory pathway towards higher levels of autonomy by mandating provisions for cybersecurity, performance validation and over-the-air (OTA) updates.

Regional Regulatory Advances around Autonomous Vehicles

At the regional level, China and Germany have taken leading roles in building regulatory frameworks for autonomous vehicles, while the United States and South Korea have also made notable progress in deployment and certification efforts.

China introduced a clear commercialization pathway for OEMs targeting Level 2-4 autonomy through its national pilot program, announced in November 2023. By focusing on seamless integration between vehicles, infrastructure and cloud platforms — leveraging technologies such as Cellular Vehicle-to-Everything (C‑V2X), edge computing, and signal systems — the initiative has ensured pilot zone vehicles are equipped for safe and standardized evaluation.

Through this initiative, Chinese OEMs have made significant progress, launching their own branded ADAS platforms — DiPilot (BYD) and G-Pilot (Zeekr) — in early 2025. BYD became the first Chinese automaker to obtain a conditional Level 3 testing license in July 2023 and has since introduced Level 4 autonomous parking capabilities through its DiPilot ADAS platform. By June 2025, nine manufacturers, including Nio, Changan Automobile, and GAC, had completed preparations for public road testing of Level 3-capable vehicles.

Germany has also emerged as a regulatory leader, particularly through the Autonomous Vehicles Approval and Operation Ordinance (AFGBV) which governs the approval, registration and operation of SAE Level 4 autonomous vehicles. While the ordinance was adopted in May 2022 and came into effect in July 2022, detailed implementation guidelines published in 2024 clarified practical procedures for public transportation authorities. These documents have provided essential guidance to municipalities, transit operators and OEMs, helping shape a consistent framework for the long-term deployment of autonomous fleets.

Guideline for Operational Area Approval: Framework for municipalities and OEMs to define, assess, authorize public road areas for autonomous vehicle operation
Handbook with Implementation Proposals for Municipal Practice: Offers integration guidance for public transport operators
Strategy for Autonomous Driving in Road Traffic: Outlines Germany’s national roadmap for autonomous vehicle (AV) deployment across mobility and infrastructure sectors

These regulatory advances have enabled OEMs such as BMW and Mercedes-Benz to integrate automation software into their vehicle portfolios. In June 2024, BMW introduced both Level 2 (‘BMW Highway Assistant’) and Level 3 (‘BMW Personal Pilot’) systems in its 7 Series lineup, offering highway automation and conditional driver delegation capabilities. In December 2024, Mercedes-Benz received approval to increase the operating speed of its DRIVE PILOT system to 95km/h and became the first automaker in Germany authorized to use special marker lights indicating automated driving mode.

Beyond China and Germany, regulatory clarity has expanded in other key regions. In South Korea, a March 2025 update to the enforcement decree of the Act on the Promotion and Support for the Commercialisation of Autonomous Vehicles enabled performance certification and approval of Level 4 autonomous vehicles, including those lacking pre-established safety standards. Similarly, the United States broadened Federal Motor Vehicle Safety Standards (FMVSS) exemptions under Part 555 in June 2025, allowing developers to deploy safety-validated autonomous vehicles that do not meet conventional design requirements.

These national and international efforts collectively signal a growing global alignment in regulatory strategy and commercial deployment readiness. Structured permit systems and clearly defined liability frameworks have provided OEMs with the flexibility to develop, certify, and scale Level 3+ autonomous vehicles — a momentum that is likely to accelerate further in the coming years.

Open-Source SDV: Software-Driven Collaboration

As the path to commercial autonomy becomes clearer, attention is increasingly turning to the software foundations that enable it to scale — particularly open-source software defined vehicle (SDV) projects. This shift is being shaped by the growing convergence of autonomous vehicles (AVs) and SDVs, where AVs increasingly rely on SDV architecture for modularity, real-time updates, and system integration. Open-source platforms are emerging as critical enablers of this transition by supporting scalable and collaborative development.

SDV platforms provide the technical backbone for scalable autonomy by enabling modular design, continuous over-the-air (OTA) updates, and real-time system integration. These capabilities, when delivered through accessible and interoperable open-source solutions, help overcome the fragmentation and integration challenges that often hinder large-scale AV deployment.

A key example of this trend is the S-CORE Project, announced in June 2025. Backed by key industry players like Bosch, QNX and Mercedes-Benz, the initiative aims to build the first open-source core stack for SDVs. The core stack is designed to standardize the middleware layer between the operating system and higher-level vehicle applications, with an emphasis on functional safety. Aligned with global regulatory standards such as ISO 26262 (functional safety), ISO/SAE 21434 (cybersecurity), and UN Regulation No. 156 (OTA Updates), the framework is OEM-agnostic and modular by design — supporting deployment across a wide range of vehicle platforms.

While it builds on a growing legacy of open-source automotive projects such as Autoware — of which AUTOCRYPT is a participating member focused on addressing security risks in real-world vehicle software — the S-CORE Project represents a meaningful shift. It moves focus from application-specific tools (e.g., AV stacks, ADAS platforms) toward foundational, certifiable infrastructure designed to support mass production of SDVs. Positioned as a “core runtime environment” for software-defined vehicles, S-CORE aims to bridge the gap between low-level system layers and OEM-specific applications, creating more room for OEMs and Tier 1 suppliers to collaborate on shared infrastructure.

Further open-source projects around software-defined vehicles are expected to emerge in the future due to economic and strategic industry alignment. With the complexity of software-defined vehicles (SDVs) increasing, it has become less viable for individual OEMs and/or suppliers to build and maintain fully proprietary software stacks. Open-source core frameworks like the S-CORE project aim to address this challenge by providing a standardized, resuable foundation which could allow companies to redirect resources toward value-added differentiation (UX, apps, mobility features).

Alignment with global regulatory standards has further elevated the role of open-source software. Standards such as UNECE R156 and R157, ISO 24089, ISO/SAE 21434 emphasize the need for secure, traceable, updateable vehicle software, better done transparently through building on open-source environments. In short, open-source projects offer a flexible and accountable framework, helping stakeholders align with evolving requirements more efficiently.

Future Implications

Regulatory and commercial developments across Levels 2 to 4 autonomy continue to mature, creating new opportunities for OEMs and Tier 1 suppliers, while steadily enhancing the autonomous driving experience for end users. This transformation is no longer confined to national borders, as open-source initiatives gain traction, driven by economic and regulatory imperatives.

As autonomous driving environments expand, so do the associated attack surfaces — from internal vehicle systems to connected external infrastructure. This underscores the growing need for continuous cybersecurity validation, including threat modeling, real-time risk monitoring and regulatory gap analysis. Positioned at the intersection of software-defined vehicle (SDV) innovation and autonomous vehicle (AV) safety, Autocrypt remains committed to supporting OEMs and Tier 1 suppliers in scaling innovation without compromising cybersecurity.

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26 May, 2025 . 7 mins read

Teleoperation Control Modes in Autonomous Driving

Autonomous driving presents the possibility of a future where individuals can engage in personal activities while traveling, without the need to focus on driving. Yet, questions remain as to whether such a future, free from manual vehicle control, will truly materialize. This blog introduces two distinct teleoperation methods designed to maximize the potential of safe autonomous driving.

The Spectrum of Autonomous Driving

As defined by SAE International, a global professional association of engineers in the automotive industry, automated driving systems are classified into six levels, ranging from Level 0 to 5.

Level 0 represents full manual control, where the driver is entirely responsible for operating the vehicle, a scenario that reflects most current driving experiences. At this stage, no autonomous technology is applied.

For Levels 1 to 2, vehicles begin to assist the driver with features such as Smart Cruise Control, Lane Following Assist (LFA) and Autonomous Parking. From Level 3, autonomous driving becomes more pronounced, with conditional automation enabled under specific circumstances.

Level 4 marks a critical milestone in the advancement of autonomous driving. While it shares similarities with Level 3 in that the vehicle can autonomously steer the wheel, the key distinction lies in its ability to manage hazardous situations without human intervention. As such, Level 4 marks the stage where “full automation” starts to materialize.

Level 5 represents the highest level of vehicle autonomy, where a car can navigate across all environments without any restrictions on an ODD (Operational Design Domain), a set of defined conditions under which an autonomous system is designed to safely operate. At this stage, “full automation” is reached.

Most of the autonomous vehicles we see around us are currently positioned at Level 3. When a situation comes where AI (Artificial Intelligence) technology fails to respond, the driver needs to take command over vehicle operations and responsibility is bestowed upon the driver in case an accident arises. The maturity of autonomous technology becomes pivotal from Level 4 where the car must proactively respond to emergency situations in a safe manner without the interception of the driver.

Currently, autonomous vehicles are not yet resistant to object misdetection as they collect information through sensor devices such as cameras, radars, and LiDAR technology. Even if all sensors around the surrounding object are properly functioning, there may be instances where AI cannot fully comprehend an untrained scenario. In this case, human control becomes pivotal, whether it comes from the driver itself or from another subject. This is where teleoperation methods become relevant.

The Necessity of Teleoperations in Autonomous Driving

Imagine a typical scenario in which you are commuting home from work in an autonomous vehicle, using self-driving mode to catch up on delayed tasks. Suddenly, the vehicle encounters a situation where the conditions necessary for safe autonomous operations are no longer met. In other words, the system is unable to function properly, requiring the driver to assume control and take full responsibility. However, with the deadline approaching and the task still unfinished, the driver may choose to request teleoperation support. In such cases, a remote operator can assist in managing the situation without requiring the driver to take full control.

Teleoperation service can also be deployed in more extreme scenarios, such as during wartime or natural emergencies. This is unsurprising, given that the origins of teleoperation technology are rooted in military applications. As early as the 19th century, efforts were made to develop remotely controlled torpedoes, and the technology has continued to be explored for defense-related purposes ever since. One notable example is inventor Nikola Tesla’s 1898 demonstration of a remote-controlled torpedo—an ambitious attempt that, despite ending in failure, marked a pivotal moment in the history of teleoperation.

The use of teleoperation in military contexts is especially pivotal, as deploying personnel in active war zones can be extremely hazardous. In such cases, teleoperated vehicles or robots can be strategically positioned to reduce risk to human life. When factoring in the use of drones, teleoperation represents one of the most dynamic and rapidly evolving areas of military technology.

Teleoperation Control Modes in Autonomous Vehicles – Direct and Indirect

Teleoperations refer to the technology that enables communication and control between a vehicle and an external location, typically coordinated through a centralized control center. In essence, when an autonomous vehicle encounters an unexpected situation that its onboard AI cannot handle, a remote operator at the control center can intervene and take effective control of the vehicle on behalf of the user.

There are two main types of teleoperation control: direct and indirect, differentiated by the level of human involvement. In ‘direct teleoperation,’ a remote operator takes full, real-time manual control of the vehicle. In contrast, ‘indirect teleoperation’ involves shared control, where the vehicle retains partial autonomy while the operator provides high-level guidance.

Automakers have explored teleoperation as a solution for complex scenarios. For example, in December 2022, Hyundai Motors partnered with Israeli startup Ottopia to develop a teleoperation system called Remote Mobility Assistance (RMA), aimed at supporting Level 4 and higher autonomous driving instances. More recently, Tesla announced they were set to launch a limited robotaxi service in Austin, Texas, by the end of June 2025, heavily relying on teleoperators to assist in situations where the autonomous system encounters difficulties.

Direct Teleoperation Control

While teleoperation holds great promise, it also presents significant challenges, particularly when it comes to direct control. One major issue arises when there are network disruptions affecting data transmission, and information sent from the vehicle to the teleoperator gets delayed or not reflected in real time. Although rare, instances of network latency or unstable communication can cause a time lag in the control center’s response, potentially making it impossible to prevent an accident.

Moreover, an overreliance on direct teleoperation can be seen as an inefficient use of the advanced capabilities built into autonomous vehicles. Given that vehicles are already equipped with advanced sensors like LiDAR, radar and camera sensors for real-time decision-making, delegating control to a remote operator may underutilize these capabilities and limit the system’s full potential.

Indirect Teleoperation Control

Recognizing the limitations of direct teleoperation, current research highlights indirect teleoperation control as a more effective complementary solution.

As the term suggests, under indirect control, the teleoperator does not directly issue commands ranging from handle steering, acceleration, or braking. Instead, high-level or abstract commands are transmitted, while the vehicle itself executes detailed actions. This approach reduces dependence on constant network communication and allows the vehicle to make better use of its internal technologies.

A primary example of indirect teleoperation control in action is “navigational route assistance,” where drivers receive guidance from the vehicle on the most optimal path to reach a specific destination. Another use case is “recognition alerts,” where the system advises the vehicle on whether to detour or disregard certain road obstacles.

While direct teleoperation is always subject to the risk of unstable telecommunications, indirect teleoperation significantly reduces this vulnerability by making the vehicle less dependent on network connections. In this mode, the vehicle makes real-time decisions autonomously, with the teleoperator offering directional input rather than direct control. All onboard components and safety systems of the vehicle remain fully active and engaged, further reducing reliance on the control center operator.

Enabling safe autonomous driving through teleoperation control

It is expected that Level 4 autonomous vehicles will interchange modes between autonomous driving, direct teleoperation and indirect teleoperation. Although skepticism persists about when Level 5 autonomy will be fully achieved, advancements in the integration of internal and external communication systems continue to accelerate, bringing the future of save autonomous driving ever closer.

AUTOCRYPT stands as a leading automative cybersecurity provider with experience in facilitating remote driving assistance environments. In particular, AutoCrypt® RODAS (Remotely Operated Driving Assistance System) provides a failsafe for autonomous vehicles by giving authority for an authorized operator to take control over a vehicle when an unexpected situation arises. This can be done either remotely (i.e. teledriving) or through configuring driving policies based on the situation reported by the occupants (i.e. teleguidance).

To learn more about the Autocrypt’s teleoperation services, click here. Read our blog for more technology insights or subscribe to AUTOCRYPT’s monthly newsletter.

12 November, 2024 . 5 mins read

EDR and DSSAD: A Look at Vehicle Accident Analysis Tools

In this age of autonomous driving technology, whenever there is an accident, heads turn to utilizing data from vehicle data recorders like the Event Data Recorder (EDR) or Data Storage System for Automated Driving (DSSAD) to uncover the accident cause. In today’s blog, we’ll take a closer look at the functions of the EDR and DSSAD, their differences, and their significance for accident analysis in the new era of autonomous driving.

It has become easier than ever to obtain recordings of vehicle accidents. With the combination of vehicle dashcams and nearby CCTV footage, determining the cause or perpetrator of an accident has become much more manageable than before. However, it can still be challenging to ascertain the root cause of an accident solely through video footage.

One particular type of accident that is difficult to analyze is the case of a sudden unintended acceleration (SUA). While the number of reported incidents has been decreasing this past decade, SUA accidents remain a frequent and often controversial topic of discussion. These types of accidents can be challenging to evaluate solely through video footage analysis, and this is where additional devices and data become necessary.

EDR

The Event Data Recorder or EDR is a type of data recording device that is embedded into a vehicle’s Airbag Control Unit (ACU) or the engine’s Electronic Control Unit (ECU). When a collision or a sudden incident occurs while the vehicle is in motion, the EDR records data related to vehicle operations for a specific period of time.

In many countries, there are stringent regulations on what the EDR is required to record. For example, in the United States, the National Highway Traffic Safety Administration (NHTSA) specifies requirements for EDRs under 49 CFR (Code of Federal Regulations) Part 563.

Source: 49 CFR Part 563: Event Data Recorders, published by the National Highway Traffic Safety Administration (NHTSA)

The EDR records critical vehicle data as listed above. In the case of an incident, vehicle owners can provide this information to authorities for accident analysis. The EDR plays a vital role in understanding accident dynamics and improving vehicle safety standards as a whole. The EDR is so vital, in fact, that in 2022 the NHTSA proposed to extend the EDR recording period from five seconds to 20 seconds.

This realization of the importance of EDRs is not limited to the United States. In 2021, the UNECE’s WP.29 (The World Forum for Harmonization of Vehicle Regulations) put into force UN R160, a regulation establishing provisions concerning vehicles and EDRs. R160 defines certain data collection and implementation requirements for EDRs. Following this, in 2022, the European Union approved a new act that requires the installation of an EDR in all motor vehicles in M and N categories (passenger vehicles and trucks). The regulation went into force in July of 2024 for all new vehicles.

DSSAD

The Data Storage System for Automated Driving (DSSAD) is a device designed to record and store data during autonomous driving sequences. It records and stores data on significant events related to autonomous driving, such as system activation, partial autonomous system failure, or minimal risk maneuvers. This data can then be used to address accidents and regulatory issues related to autonomous vehicles.

While DSSADs are only mandated in a handful of countries, their implementation is subject to certain regulatory measures for compliance. For instance, UNECE’s UN R157, which covers automated lane-keeping systems (ALKS), mandates DSSAD for vehicles equipped with ALKS in order to monitor status changes in the autonomous driving system (ADS).

Comparison of EDR and DSSAD

While there are similarities between EDR and DSSAD, there are core differences between the two.

The EDR is primarily designed for investigation of conventional vehicles, while the DSSAD is specifically developed for autonomous and semi-autonomous vehicles.
The EDR stores and provides data related to accidents just before they occur, while the DSSAD will store autonomous driving-related data for a relatively long period.
EDR data is only stored temporarily, and is not typically retained unless a crash occurs, while the DSSAD data is retained for a longer timeframe (typically around six months), or up to a certain number of recorded events to ensure comprehensive documentation.

Despite the differences, the two complement each other in analyzing accidents and clarifying liabilities regarding an incident. A vehicle’s dashcam has limitations, so the EDR can be crucial for accident analysis. Regulations regarding DSSAD in autonomous vehicles can also clarify responsibility between driver(s) and the vehicle.

In today’s era of autonomous driving technology, both the Event Data Recorder (EDR) and the Data Storage System for Automated Driving (DSSAD) are gaining significant attention due to growing concerns about liability in the event of accidents. However, this also brings forth the issue of cybersecurity. Maintaining data integrity is essential, as both the EDR and DSSAD store and retrieve data that could influence accident investigations. Tampering with this data could not only hinder accurate accident analysis but also allow parties to misplace liability. Security measures such as data anonymization and encryption are vital for protecting sensitive information stored by the EDR and DSSAD, as well as safeguarding personal data, location information, and driving records.

EDR and DSSAD are vital tools for transparency and accountability in autonomous vehicles, but their effectiveness hinges on comprehensive cybersecurity. By implementing robust protections against data tampering and unauthorized access, these recording technologies can serve their intended purpose: helping investigators understand complex accidents, advancing autonomous driving technology, and building public trust. The path to widespread adoption requires both sophisticated data collection and unwavering security measures.

Navigating the evolving mobility landscape is complex, but cybersecurity will play a key role in building trust among manufacturers, consumers, and legislators, ultimately paving the way for a secure future.

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