The 6 Levels of Autonomous Driving Explained

Imagine a world where your car doesn't just take you places but also makes decisions, navigates complexities, and ensures your safety - all by itself. Welcome to a journey into the future of transportation – "The 6 Levels of Autonomous Driving Explained". In this blog, we will explain all autonomous driving levels, from the essential assistance of Level 0 to the fully independent marvels of Level 5; each level unfolds a new chapter in the story of how vehicles transform from mere modes of transport to intelligent, self-driving entities. 


Source: SAE International

What is Autonomous Driving?  

Autonomous driving is a transformative technology that allows vehicles to operate and navigate independently without human intervention. This innovation marks a significant shift in vehicle control, as it combines hardware and software to understand the driving environment, make informed decisions, and carry out driving tasks.   

The SAE J3016 (Society of Automotive Engineers) standard categorizes driving automation into six levels, ranging from Level 0 to Level 5. Levels 0 to 2 are classified under 'Driver Support, involving systems where the driver plays a significant role. Levels 3 to 5 fall under 'Automated Driving, ' where the vehicle takes on more driving tasks. Currently, most commercially available driving automation features are at Level 2 or lower, which falls within the realm of driver assistance. 


6 Levels of Autonomous Driving  

6 Levels of Autonomous Driving

Source: SAE International

Level 0: No Automation 

At this base level, the human driver performs all driving tasks. The car may have simple warnings or momentary assistance (like emergency braking), but control remains firmly in human hands. Think of a regular car with some basic safety features. 


Level 1: Driver Assistance 

This level represents the first step in the journey towards fully autonomous vehicles and is also the lowest level of autonomy. At this level, the vehicle can assist with either steering or acceleration/deceleration, but not simultaneously.   

Level 1 enhances safety and driving convenience by integrating features like adaptive cruise control (ACC) and lane-keeping assist (LKA), which help maintain a steady speed and keep the vehicle within its lane. However, at this level, the driver remains entirely in control and must be actively engaged in the driving process, as the technology only serves to assist rather than replace the human driver. Level 1 features are standard in many modern vehicles, marking the initial step towards more advanced autonomous driving technologies.   

Level 1 autonomous features are widely available in many modern vehicles across brands such as: 

  • Toyota: Toyota Safety Sense suite 
  • Honda: Honda Sensing technology 
  • Ford: adaptive cruise control. 
  • General Motors: adaptive cruise control 
  • Nissan: Nissan's ProPILOT Assist  


Level 2: Partial Automation  

Level 1 and level 2 are ADAS (Advanced Driver Assistance Systems). In other words, this level sees vehicles capable of controlling both steering and acceleration/deceleration. While the vehicle can handle specific tasks, Level 2 systems do not negate the need for an attentive driver. The driver must keep their hands on the steering wheel (or be ready to do so) and be prepared to intervene immediately if the situation requires it.  

Level 2 automation is particularly useful in highway driving scenarios, where the conditions are more predictable, and the systems can effectively maintain lane position and speed. However, the driver must fully engage in complex environments like urban streets or adverse weather conditions.   

Here are some notable companies with Level 2 autonomous driving vehicles: 

  • Tesla: Autopilot system 
  • General Motors (Cadillac): super cruise system 
  • Mercedes-Benz: driver pilot 
  • BMW: Driving Assistant Plus system 
  • Audi: Traffic Jam Assist 

Learn More About ADAS


Level 3: Conditional Automation  

Transitioning from Level 2 to Level 3 autonomous driving represents a significant step in developing autonomous vehicle technology. This leap involves substantial changes in the vehicle's systems and the driver's role. Vehicles at Level 3 can perform all driving functions under certain conditions, such as on a highway or in traffic jams, including steering, accelerating, braking, and monitoring the environment. However, the driver must be ready to take control when the system requests. The system will request the driver to take over when it encounters a scenario beyond its capabilities or when exiting the conditions it's designed for (e.g., leaving a highway).  

Level 3 autonomous driving relies on an advanced suite of sensors, such as cameras, radar, and LiDAR, paired with potent AI and machine learning for intricate decision-making and swift data processing. Improved connectivity, like V2X, extends the vehicle's awareness of its surroundings. Safety is strengthened with backup systems and driver monitoring to guarantee the driver is ready to take over when needed. This level signifies a notable move towards increased vehicle autonomy, shifting the driver's role to more of an overseeing function. 

Honda received approval for Level 3 functionality in March 2021 but was limited to operating only in Japan. Meanwhile, Mercedes-Benz achieved a milestone in December 2021 by getting the first international approval for Level 3 operation. This approval was based on passing the R157 regulatory test by UNECE, under the regulation known as 'Automated Lane Keeping Systems (ALKS)'. Released in June 2020 and approved by 54 countries since January 2021, ALKS sets various conditions necessary for autonomous driving, a crucial step in the global standardization and advancement of automobile automation. While SAE J3016 defines driving automation levels, ALKS outlines the specific requirements for operating autonomous vehicles. 

Here are few companies with commercial level 3 autonomous cars: 

  • Audi ( A8): Traffic Jam Pilot 
  • Honda: Traffic Jam Pilot 
  • Mercedes- Benz: Drive Pilot 


Level 4: High Automation  

Level 4 of autonomous driving, often referred to as "High Automation," marks a significant leap forward in the evolution of self-driving technology. At this stage, vehicles can operate completely autonomously in specific conditions or environments without human intervention. However, it still has the option for human control.  

A common feature of Level 4 vehicles is geofencing, where the autonomy is restricted to a specific geographic, while V2X communication enhances situational awareness. Level 4 vehicles, capable of dynamic path planning and navigation, operate without driver intervention in their designated conditions, although they may retain manual controls for use outside these areas. Enhanced safety and cybersecurity measures are crucial to protect against potential threats in these highly autonomous environments. 

Level 4 autonomy was primarily being tested and deployed in specific applications (autonomous vehicles, shuttle services) rather than being widely available to individual consumers.  

  • Waymo: Waymo is one of the leaders in Level 4 autonomous technology, operating its Waymo One service. This service uses Level 4 autonomous vehicles primarily for a public ride-hailing service in designated areas like Metro Phoenix, San Francisco, and is ramping up in Los Angeles County and Austin, Texas. 
  • Navya: a French company specializing in the development of autonomous driving systems, did offer a shuttle service using vehicles that are often considered to be at or near Level 4 autonomy. Navya's autonomous shuttles are designed primarily for use in controlled environments, such as private sites, campuses, urban centers, or specific public roads designated for autonomous vehicles.


Level 5: Full Automation 

Level 5 autonomous vehicles represent the zenith of self-driving technology, capable of operating independently in any scenario a human driver can handle. This encompasses driving seamlessly in diverse and challenging environments, from bustling urban streets to highways and under various weather conditions, whether under the bright sun or through heavy rain and snow.   

In their most advanced form, Level 5 vehicles could eliminate the need for traditional driving controls such as a steering wheel, accelerator, or brake pedals. However, as of now, this level still needs to be more aspirational than practical.  

While some progress has been made in Level 4 autonomy, with a few companies developing highly automated vehicles, these still need to be broadly available to consumers. Advancing to Level 5, where a car could flawlessly navigate any global location dropped into, is a significant leap that hasn't been achieved. 


What Are the Challenges for Reaching Fully Autonomous Driving? 


Technical Litmitations

Achieving Level 5 autonomy is technologically challenging due to the need for advanced sensors and perception technologies that can accurately interpret complex driving environments under various conditions. Additionally, developing reliable software algorithms capable of making safe decisions in unpredictable scenarios and learning from new experiences is a significant challenge. Ensuring the effectiveness of vehicle-to-vehicle and vehicle-to-infrastructure communication is also crucial. 


Safety and Security Concerns 

Ensuring the cybersecurity of autonomous vehicles is crucial to protect them from hacking threats. Establishing comprehensive safety standards and testing protocols is essential to guarantee that these vehicles can handle all driving situations safely. This encompasses both the physical safety of passengers and the security of the vehicle's connected systems. 


Infrastructure Adaptation 

Full autonomy requires significant changes to existing road infrastructure, such as smart traffic systems and compatible lane markings. This adaptation demands substantial investment and planning. Additionally, urban planning needs to consider the impact of autonomous vehicles on traffic flow, parking, and overall city layout. 

Economic and Business Model Challenges 

The development and production costs of fully autonomous vehicles are high, which could limit their accessibility. Additionally, the automotive industry may need to shift from traditional ownership models to service-based models, such as ridesharing or vehicle-as-a-service, to align with the nature of autonomous vehicle use and ownership. 


Regulatory and Legal Issues 

Creating a legal and regulatory framework for autonomous vehicles involves addressing usage, liability, and insurance complexities. Differing laws across regions and countries compound this challenge. Moreover, ethical considerations, such as decision-making in unavoidable crash scenarios, need to be addressed. 


What’s Next for the Future of Autonomous Vehicles 

The future of autonomous vehicles (AVs) is marked by significant growth and transformation, with the autonomous vehicle market projected to reach $300 billion to $400 billion by 2035, and the ADAS and autonomous driving market anticipated to hit $55 to $80 billion by 2030. These figures, reported by ResearchandMarkets and McKinsey, respectively, signal an upcoming boom in the popularity and adoption of AVs. This growth is underpinned by advancements in technology, increased safety, changes in urban infrastructure, evolving regulations, and environmental benefits. The integration of AVs is set to reshape transportation, urban planning, and various economic sectors, offering increased accessibility and personalized travel experiences, while also posing new challenges in ethics, societal acceptance, and professional employment in driving-related fields. 

Explore More Smart Transportation Solutions 


Premio’s AI Edge Computers for Autonomous Vehicles 

Level 2 to Level 4 Autonomous Vehicle Computers 

With its innovative Machine Support Training solution, Premio has taken a significant leap forward instarting with advancing in ADAS (Advanced Driver Assistance Systems), utilizing its state-of-the-art AI Edge Inference Computers from the RCO-6000-RPL Series. These computers are specifically engineered to capture and store vital data from vehicle cameras and sensors, playing a crucial role in developing and training ADAS systems for Level 1 and Level 2 of autonomous driving. As the AI performance accelerators become more powerful, the scalable RCO-6000-RPL Series can be customized with NVMe, DDR5, and NVMe expansions for level 4 autonomous vehicles applications, which have been proven and deployed in some parts of the world. 

Explore ADAS Data Capture and Storage Computers

AI Edge Computer  for Autonomous Vehicles


Key Feature of RCO-6000-RPL Series: 

  • 12th/13th Gen Intel® Core™ Processor with R680E PCH (35W TDP) 
  • Blazing-Fast DDR5 with ECC Support 
  • MIL-STD-810G Compliance on Method 516 & 514 for Shock and Vibration 
  • Edge AI Ready with multiple Hailo-8™ (26 TOPS / 2.5W) 
  • High Performace GPU expansions with 20G Shock and 3 Grms Vibration resistance 
  • Hot-swappable NVMe/SATA storage with PCIe Gen 4 supports  
  • Configurable EDGEBoost I/O Modules for versatile IoT sensor connectivity 
  • Mix & Match EDGEBoost Nodes for AI Acceleration and Training 

Explore the RCO-6000-RPL Series