# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 2 Terminology
### Operational Design Domain (ODD)  
According to the Society of Automotive Engineers (SAE) J3016 [7], the operational design domain (ODD) of a driving automation system is defined as the \"Operating conditions under which a given driving automation system, or feature thereof, is specifically designed to function, including, but not limited to, environmental, geographical, and time-of-day restrictions, and/or the requisite presence or absence of certain traffic or roadway characteristics\" [8]. In other words, the ODD specifies the particular conditions and environmental scope within which the autonomous driving system can operate, thereby establishing constraints on the driving environment for a given test scenario.  
To ensure autonomous vehicles function effectively in their intended environments, it is crucial that their validation includes training data and testing that cover all relevant operating conditions. A common approach to achieving this involves restricting the vehicle's operational environment to a subset of scenarios that a human driver might encounter. This approach, known as adopting an operational design domain (ODD) [9], helps define the system's operational scope. An ODD can also limit the road environment, AV behavior, and vehicle states [10].  
An ODD can also impose restrictions on the road environment, vehicle behavior, and vehicle states [10]. In [10], the road environment for autonomous driving was categorized into urban, rural, or highway settings; intersections, roundabouts, tunnels, construction zones, parking lots, narrow alleys, and narrow roads; as well as specific conditions like rainy, snowy, foggy weather, and nighttime driving, based on road types, structural elements, temporary modifications, traffic flow, weather, and visibility. Beyond road environment limitations, an ODD may also specify constraints on the behavior and state of autonomous vehicles, such as speed limits, restrictions on certain maneuvers like reversing, and limitations related to vehicle loading.  
According to [11], six top-level categories are used to define and classify ODDs, which are physical infrastructure, operational constraints, objects, connectivity, environmental conditions, and zones. There are various types of ODDs, encompassing scenarios such as car-following; lane changing; turning around; overtaking; navigating intersections; driving on roundabouts, highways, and urban roads; operating in construction zones and tunnels; maneuvering in confined spaces; driving in adverse weather conditions; and responding to emergencies, among others. Consider lane changing as an example. On a straight two-lane urban road marked by a dotted line, with clear weather and no pedestrians, vehicles are traveling in both lanes. The autonomous vehicle (AV) is in one lane, while another vehicle (BV) occupies the other lane. Both vehicles are moving at a speed of 60 km/h. In this scenario, the AV intends to change lanes from its current lane into the lane occupied by the BV. Figure 1 provides an illustration of this ODD. Another example is a car-following ODD, illustrated in Figure 2. In this scenario, on a straight, two-lane highway with dotted line markings and cloudy weather, there are no pedestrians, and vehicles in both lanes are traveling at 100 km/h. In one of the lanes, an AV follows a BV at a constant speed.  
Figure 1: Lane-changing ODD representation.  
Figure 2: Car-following ODD Representation.