Technology Used In Artificial Intelligence – Over the past decade, The automotive industry has seen rapid growth in Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) solutions, driven primarily by the advancement of AI. in fact, The autonomous vehicle is often cited as an example of AI.
Artificial Intelligence involves various technologies and they must be implemented to achieve the desired solutions. An AI technique or combination of those described in Figure 1 can help implement the components or features required for Autonomous Driving.
Technology Used In Artificial Intelligence
The first is a modular approach where multiple features and non-AI-based features and core components are developed separately and integrated together, e.g. The Object Detection (AI) module is complemented by other sensor data processing modules that are non-AI modules such as Object Tracking and Fusion. Another school of thought incorporates an ‘end-to-end approach’ driven solely by AI, so data-driven it is called “Data-Driver”.
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Because the end-to-end or data-driven approach is driven by AI; Simultaneous input sensor data (Camera, Radar, LiDAR, GPS, IMU) needs to be marked and labeled in a simple way that correlates with simulation signals. steering brake etc. This combination provides the necessary instructions or action to move the vehicle. In this method, The entire development process becomes easy and the end-to-end approach is desirable among researchers. However, This method suffers from reusability and maintainability because it is created with a configuration of sensor data or a configuration of sensor data with specific annotation guidelines. Adapting this approach to multiple use cases requires repeating the complete process to create an appropriate AI model. Most importantly, It becomes difficult to debug and track the system for potential vulnerabilities or malfunctions. It loses its determinism because of the complexity of the many conditions that contribute to it.
Therefore, The industry prefers a ‘Modular Approach’ where a complete system is designed to combine multiple features and components. In this way, Each feature and its underlying components can be checked and analyzed for defects at both the software level as well as the hardware level for desired functionality. Therefore, Appropriate surcharges can be built in as a ‘failure to act’ mechanism to ensure that the vehicle leaves the main route and parks safely in the event of a fault. This is called the Minimum Risk Shift. Autonomous Driving system is a safety critical system under Functional Safety Standard – ISO26262, so it is most important.
Let us now take a closer look at the basic structures or modules of the Autonomous Driving system architecture.
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The environment (scene) perception module is responsible for sensing and measuring the surrounding environment in 3D, real world coordinates. Deep Learning is well suited for this task. Today, Real-time Deep Learning (DL) or Machine Learning (ML) architectures are used for 2D and 3D object detection and recognition in a rigorous manner. Another DL approach, Semantic Segmentation, is based on segmentation of vehicle traffic. Useful for understanding scenes that refer to traffic objects and related but not strict obstacles in the scene. Such multi-sensory integration is done using conventional techniques through probabilistic reasoning. Spatial detection confidence and temporal continuity of detection are essential metrics for such a synthesis. Although the detection methods in the applications are similar; The sensor technology (Camera, LiDAR, RADAR, Ultrasonic sensor) and its structural topology make the application different for each vehicle manufacturer (OEM) / and their suppliers (Tier1s). Needless to say, It also requires modifying and tuning the adopted AI methods with reference to the selected hardware.
A vehicle with autonomous driving features, also known as an Ego Vehicle, derives its location from the perception updates in the 3D environment as well as mapping information through the positioning and mapping module. Map information can be processed by processing data from sensors or extracted from HDMap. Although there are some improvements in the prior art for deep learning-based localization; Traditional SfM (Structure from Motion) techniques still dominate embedded platforms due to their computational efficiency. These methods are basically coupled with captured vehicle odometry signals using inertial measurement units.
This is a heuristic analysis and modeling technique for time-series data generated by perception and localization and is therefore originally a rule-based course. In general, Traditional AI techniques for random variable processes, such as Markov Chain modeling and its variants, used heavy computations and large memory requirements to store the relative existence of each user. Even constructing an acyclic graph directly to depict relationships across specified input variables is very imprecise and time-consuming. This adds complexity as many direct and derived variables are considered to generate an answer. Therefore, Various machine learning and deep learning techniques such as temporal sequencing modeling, sequence-to-sequence modeling; regular
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Neural Networks (Long Short Term Memory) are adopted to form a deterministic and general relationship between input and output. It has the advantage over the traditional method of establishing the correlation of traffic occupants and ego vehicles on a temporal data set used to train an AI model.
Figure 3: Neural Network generates output to help understand the location of the Ego Vehicle and where the previous vehicle will be.
A last but very important module of route planning and arbitration of ego vehicle behavior is non-trivial source-to-destination planning to identify a non-trivial route by analyzing the adaptive environment and context from the non-trivial source to the destination. I am torn between several options. Therefore, An ego vehicle goes straight; turning left or right; traffic jams; overtaking An autonomous vehicle is an unstructured multi-agent environment that utilizes coordination capabilities with other traffic controllers to generate a contextual driving policy for yield etc. Deep reinforcement learning and simulation learning techniques, which respectively record real driving data with reference to the simulated environment, have their own advantages and disadvantages. This paves the way for traditional AI approaches suitable for different types of autonomy in different contexts. Routing that takes the results of this routing as input is usually dominated by traditional model-based control techniques, but traditional approaches generate control functions by solving an optimization problem with a large number of fixed variables. However, The meta-constraints require combining the A-Priori Model (traditional model) with the new time series analysis AI model (non-linear dynamic model) to achieve stability with less design effort.
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So, various AI techniques are used to build all the modules or parts of the AD system and it goes to the next step of making sure it works in the vehicle. The task of making an HR solution work seamlessly in a vehicle comes with its own set of challenges and is another area where AI is being used extensively.
Powerful GPU servers handle data processing; Although it is available to perform most of the AI functions offline from training. The solution is not a trivial task, especially with machine learning and deep learning using AI. Trimming (accuracy with minimal calculations) when a vehicle needs to use cutting edge materials; Power consumption and price become bottlenecks. In addition to computational optimization efforts, the memory footprint (the number of learned model parameters) is important in autonomous driving. Therefore, Designing small and efficient architectures and optimizing state-of-the-art architectures for real-time execution with AI model compression techniques; channel compression; weight reduction analysis; Many semiconductor vendors (Intel, Nvidia, TI, Renesas) offer a comprehensive tool chain for their hardware architecture (CPUs and Graphics Processing Units / Neural Processing Units) that facilitate auto-tuning of AI components to reduce time to market. Some of the leading organizations in AD are FPGA for AI solutions; It is proposed to use ASIC and adapt to its advantages. It is mainly proposed for the perception solution which leads to benefits in terms of power consumption and operational security.
In summary, AI plays a key role in autonomous driving and is shaped to meet domain-specific challenges: small memory footprint and precision; functional safety; Authentication and authentication and computing at the Edge for it at the same time. Time to provide a credible solution in various fields;
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We implement all these AI technologies in many customer applications. deep domain knowledge; The combination of AI expertise and exposure to key end user ADAS/AD programs from L2 to L4 and beyond makes it the partner of choice for autonomous driving development and related systems integration.
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