In the automated driving development journey, verification and validation (V&V) coverage is one of the most intensely discussed topics, especially scenario-based or data-driven validation. Building realistic scenarios is a key challenge where the critical requirement is to have realistic scenarios of road conditions (Long roads), like traffic behaviors, weather, time of day and many more.
To overcome the challenge of creating highly realistic scenarios, KPIT has built a process/framework to convert drive data into testable scenarios to help execute high-quality and accurate testing. The actual test drive data is taken for analysis and interesting events for the Automated driving functions are mined. These interesting events are taken further for processing and conversion into synthetic scenarios. AI Technologies like, SLAM is used for vehicle trajectory identification, Deep Learning algorithms are used for identification of relevant traffic participants, their classification and trajectory estimations.
Let’s look take a detailed look at the various processes and AI techniques used to extract and derive high-quality scenarios that assure V&V coverage for automated driving along with exhaustive use case testing
The Driving logs are analyzed to identify critical or interesting events that happened during real road testing. The identified critical events are then processed to create or derive synthetic scenarios that can be used for validation through simulation (data driven validation).
Figure 1: High level workflow
Figure 2: Step wise workflow of the process
Data from various sources is required to help derive scenarios from real world data
Table 1: Input data requirement
The methodology for scene derivation depends on the input data availability.
Figure 3: Workflow of scene creation based on data availability.
If most of the inputs as required in the Table 1 are available then the process becomes simple and highly automated, with the help of parsers and automation scripts the scenarios can be converted into synthetic scenarios from the real-world drive data.
Typically, this is not the case and the inputs provided are limited and thus the process becomes more complex. This is when AI helps in streamlining the process.
Consider an example where only video data is available as input without other data like sensor or vehicle logs. In this scenario AI technique like Simultaneous Localization and Mapping (SLAM) can be used for deriving subject vehicle parameters. SLAM uses a reference point in given plan to construct or updating a map of an unknown environment while simultaneously Keeping track of an agents (for example subject vehicle) location within it. With SLAM we will be able to track the subject vehicle and derive its location and position information. SLAM also has some limitations i.e. it cannot be used on longer sets of data, as the error of estimation increases. To Avoid this the data set is segmented, calculated for smaller segments, and then stitched back with minimal computational errors.
Having solved the problem of subject vehicle estimation the next challenge would be deriving traffic parameters and road geometry This can be mitigated using Deep Learning based algorithm to derive traffic parameters like its position, classification, speed etc. and road parameters like lane information. A well-trained DL algorithm will be run on the video to identify the required traffic participants and road information.
All these parameters are then mapped to respective tags in the OpenDrive and OpenScenario with right transformations to generate a scenario file. This scenario file will be then used in a simulator to visualize the scenario. With the availability of the Camera configuration parameters (Intrinsic and Extrinsic) used for data collection, a degree of accurate scenario models can be generated.
Figure 4: Sample image of Open Drive format (XODR)
Figure 5: Sample image of Open Scenario format (XOSC)
The above techniques help in generating high quality realistic scenario models for real road data, which can be used for Validation. The validation technique can be closed loop testing of an AD stack or an AD function.
In summary, implementing these AI techniques for scenario creation from real world data helps generate high quality scenarios and additionally brings multiple benefits like:
Overall, the high quality scenarios generated from these techniques will help reduce the need for on road testing as most of the regression testing for upcoming AD stacks and functions can be done in the simulation environment.
Associate Solution Architect | AD/ADAS Practice
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KPIT is a leading independent software development and integration partner helping mobility leapfrog towards a clean, smart, and safe future. With 7000 automobelievers across the globe specializing in embedded software, AI, and digital solutions, KPIT accelerates clients’ implementation of next-generation technologies for the future mobility roadmap. With engineering centers in Europe, the USA, Japan, China, Thailand, and India, KPIT works with leaders in automotive and mobility and is present where the ecosystem is transforming.
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