Ten Essential Categories for Scenario Variation

Developing autonomous vehicle (AV) stacks capable of safely navigating real-world environments involves rigorous training, testing, and validation against countless realistic scenarios. Each scenario must be represented in numerous variations to comprehensively assess and ensure AV stack safety and performance. This complexity underscores the critical importance of systematic scenario variation and management within the AV development process.

Effective scenario variation goes far beyond minor adjustments or changes in the weather. After truthfully replaying a real-world drive, or generating fully synthetic scenarios of rare and dangerous edge-cases, in a simulated environment, developers must thoughtfully generate diverse and realistic variations, encompassing both behavioral dynamics and environmental conditions. Achieving this level of scenario diversity requires intelligent automation and sophisticated simulation technology, particularly at higher levels of autonomy where AV stacks must perform reliably without human intervention, and complex operational design domains (ODD) such as urban driving environments.

Let’s examine ten examples of essential categories for scenario variation (listed below in no particular order), highlighting key considerations for AV developers aiming to achieve robust safety and performance validation:

1. Number and Types of Other Vehicles

To thoroughly test AV stacks, scenarios must include a variety of vehicles moving in multiple directions:

• Additional vehicles traveling alongside or in the opposite direction to the AV.
• Diverse vehicle types, from motorcycles and passenger cars to trucks, SUVs, bicycles, and even unconventional vehicles such as tractors or horse-drawn carts.

2. Variations in Maneuvering and Vehicle Dynamics

Realistic variations in vehicle dynamics are crucial:

• Vehicles performing cut-ins, overtakes, merges, turning, crossing paths, and lane changes.
• Varied speeds and realistic following distances between vehicles.
• Constraints ensuring scenarios remain physically plausible, for example avoiding unrealistic events such as a semi-trailer truck overtaking the autonomous vehicle (Ego) at 300 mph and cutting in less than 5 meters in front.

3. Other Vehicle Behaviors

Human driver behaviors in other cars vary widely and significantly affect AV interactions:

• Scenarios should include overly cautious, aggressive, distracted, or impaired drivers.
• Variations involving drunk driving, distracted driving, or sudden erratic maneuvers to test AV reaction capabilities under challenging conditions.

4. Number and Diversity of Pedestrians

Pedestrian, also known as VRUs (Vulnerable Road Users), interactions are inherently complex and varied:

• Scenarios should range from solitary pedestrians to groups, including individuals with pets, children, or elderly companions.
• Representation of diverse demographics: varied age groups, genders, body types, race, clothing styles (dark or reflective clothing, traditional or religious attire), and accessories like backpacks or hoodies.

5. Pedestrian Behavior Variations

Pedestrian actions can dramatically influence AV decision-making:

• Behaviors including running, loitering, sudden stops, and unexpected crossing movements.
• Simulating unpredictable pedestrian behavior to evaluate AV adaptability and responsiveness such as a child chasing a ball from between parked cars.

6. Animal Encounters

Animals introduce additional unpredictability:

• Testing scenarios may include encounters with domestic pets (dogs, cats), livestock (horses, cattle), and wild animals (deer, bears, elephants), although these often depend on the ODD.
• Variations involving different sizes, speeds, and behaviors of animals are critical to ensure AV systems handle these interactions safely.
• A scenario that is often overlooked is “flocks of birds”. When an AV in an urban environment drives up to a flock of pigeons who all then take off at the same time, the AV can face a literal and metaphorical shit-storm 🙂

7. Stationary Objects on the Road

Stationary obstacles present distinct perception challenges:

• Variations may include parked vehicles, construction equipment, traffic signs, advertising boards, and various types of gates or barriers.
• Inclusion of static or dynamic obstacles of varying shapes, sizes, colors, and reflective properties such as plastic bags, rocks or a couch that has fallen off a truck are equally relevant to validate the AV system.

8. Lane Configurations:

Roadway designs significantly impact AV performance:

• Testing variations including single-lane rural roads, multi-lane highways, complex urban intersections, and dynamically changing lane conditions.
• Junction variations from different locations on the ODD map – for example there may be multiple configurations of a right turn at different locations on the map.
• Diverse road/lane markings and lack thereof, often a problem in my neighbourhood!

9. Weather and Visibility Conditions:

Environmental conditions substantially affect AV sensors and decision-making:

• Scenarios should range from clear conditions to heavy rain, snow, fog, dust, glare, and low-light, twilight or nighttime situations.
• Testing AV stack performance across diverse weather events ensures robust sensor performance and accurate object detection under varied visibility conditions.

10. Environmental Diversity:

Finally, varying the broader environmental context is essential:

• Inclusion of urban, suburban, rural, and industrial environments.
• Variations in landscape elements, such as trees, buildings, fences, and commercial signage, influence AV perception and decision-making.
• Realistic shading and lighting variations to further test sensor systems.

The complexity outlined above clearly illustrates the non-trivial challenge faced by AV developers when generating meaningful scenario variations of their existing drive logs. It is evident that manual creation or superficial scenario changes fall drastically short of what’s required to adequately validate advanced AV stacks, a challenge that grows exponentially when requiring realistic data for AI-powered AV stack training.

Addressing these challenges effectively requires a closed-loop toolchain. By defining an ODD coverage plan, evaluating existing drive logs and measuring the current ODD coverage, Foretellix’s Foretify is able to reveal the safety gaps that need addressing and apply advanced scenario generation technology to close them. The Foretify toolchain incorporates intelligent constraint solvers that systematically create extensive yet feasible and relevant scenario variations. Automating the generation of robust, diverse, and physically plausible scenario variations either from the existing real-world drive logs or for fully synthetic simulations dramatically reduces development time while improving the system robustness. 

The scenarios generated by Foretellix’s Foretify can either be used to validate the AV stack or as training data for AI-powered AV stacks, greatly shortening the required development time and ensuring comprehensive coverage of the ODD. By leveraging Foretellix’s Foretify toolchain, AV developers and manufacturers can confidently achieve comprehensive training, testing and validation, essential steps toward realizing the full potential of safe and reliable autonomous mobility.

Contact us to get a demo and learn more about how we generate scenario variations within the Foretify toolchain.