Autonomous driving is changing the landscape not just for the automobile, but for the entire transportation industry. While many expect fully self-driving vehicles to hit the road soon, the challenges of making this a safe reality are proving more difficult than expected. Automakers and regulators have realized how incredibly difficult it is to fix a problem in a truly reliable way.
So, instead of solving the problem as a whole, the goal was to break it down into several stages, ranging from level 1 to level 5. Today, we can find self-driving capabilities on highways with amenities highly standardized and an ambition to continue to improve these capabilities and eventually apply them to more difficult driving situations such as country roads.
As a step towards fully autonomous driving, automakers are adding increasingly sophisticated Advanced Driver Assistance Systems (ADAS) to their vehicles. This offers features like automated parking, automated lane change, adaptive cruise control with stationary hold, blind spot monitors, driver monitoring system, collision avoidance, sign recognition signaling and many more. Automakers value these features because they help differentiate products. Drivers find many features really useful. And regulators love them because they can improve road user safety now and provide a platform to mandate additional safety features in the future.
Development of ADAS functionalities
The development of ADAS functionality is also part of a steady progression towards true vehicle autonomy. It begins by breaking down this very large overall challenge into a series of point challenges that can be tackled separately. Many of these solutions share a common thread: they have an almost insatiable thirst for data from multiple sensors. This data provides the raw material fused by powerful on-board computers to create a real-time internal model of the environment in which the vehicle is moving. This, in turn, can be used as the basis for making decisions about how to react to this pattern. Once the decisions are finalized, commands can be executed to implement the chosen reaction in the real world.
All of this data collection, sensor fusion, modeling, decision making, and command execution must happen in real time with very high levels of determinism and the utmost respect for safety. And so, one of the practical challenges of implementing ADAS functionality is finding parts that have the necessary levels of functionality, speed, and quality for use in safety-critical systems in cars.
Figure 1 Specialized chips are required to manage a plethora of smart sensors in ADAS designs. Source: Infineon
Components operating in real-time scenarios in ADAS for vehicles include power management integrated circuits (PMICs) designed to provide well-regulated power to safety-related sensors, transceivers, and microcontrollers. And microcontrollers must provide more computing power for sensor fusion applications.
There are also LIN and CANbus transceivers, specialized power regulators and protection switches, and smart power switches that can be used to replace mechanical relays. Many of these parts are available in variants that meet the requirements of the US Automotive Electronics Council (AEC) Q100 Grade 1 Component Validation Scheme. Some of the microcontrollers have also been shown to meet the requirements of the ASIL-D functional safety specification, the most stringent level of this scheme.
All of these parts should be backed up with rich documentation, tool support ranging from compiler tool chains and automatic code generation for microcontrollers to third-party support for debuggers, timing analysis, benchmarking, simulation, modeling, etc.
Many aspects of ADAS functionality can be implemented using standard components that have been adapted and qualified for automotive use. Some, however, have design requirements that can be met using new components developed to meet that specific need.
One of the most important requirements is data logging which keeps track of the streams of information passing through the ADAS computers to give them a rich view of the environment in which the vehicle is moving. Some developers use large solid-state devices (SSDs) as a place to dump all that data. However, SSDs have slow write speeds and wear-and-tear mechanisms, which may not provide adequate performance and reliability.
FRAM for data logging
One of the primary reasons for performing real-time mass data logging in vehicles with ADAS capabilities is to capture what happens when something goes wrong. So developers can learn from it and prevent it from happening again. For example, some ADAS implementations are not effective at identifying pedestrians, especially at night. Rigorous data logging, in which all relevant sensor data is captured, timestamped and stored in real time, can provide a snapshot of what was happening at the time of an incident. It can then be used to drive an ongoing model refinement process that improves ADAS implementation.
Non-volatile ferroelectric random access memory (FRAM) can perform this function. Unlike traditional nonvolatile memory technologies such as flash and EEPROM, FRAMs, also known as FeRAM, have fast write times, support instantaneous non-volatility, are as easy to address as SRAMs, and most importantly, have nearly unlimited endurance, reaching 1014 read/write cycles.
Their only downside is that they don’t have as much density as other types of memory. However, in practice, this problem is mitigated by using FRAM as a spinning buffer for ADAS data streams. If a vehicle loses power, the FRAM data log will contain a snapshot of the data available to ADAS computers up to that point.
Figure 2 FRAM scales well to data logging needs in an ADAS design. Source: Infineon
Additionally, there are no mandates yet on how much data a vehicle must save in the event of a catastrophic failure. However, SAE International already offers guidelines on what type of data to store. Similar specifications are under development in Europe. But it seems likely that developers will be asked to ensure their vehicles can capture and store 5 seconds of data on issues such as acceleration, braking effort and seatbelt status, as well as four frames per second for 5 seconds from each of the onboard cameras.
Data logging memory should understand what happens when vehicles have problems and provide the information needed to improve ADAS functionality, so it doesn’t happen again. One of these memory chips is EXCELON FRAM; it is intended for data logging applications in ADAS and has been qualified to the ASIL-B functional safety standard.
Safety comes with design rigor
Many ADAS features are designed to increase the safety of vehicles and their passengers by using sophisticated sensor fusion and machine learning techniques to avoid dangerous issues. It should be remembered, however, that these security features are the result of the rigor with which chipmakers develop manufacturing processes, products, and supporting tools and software. It also involves ensuring that components meet relevant standards and qualifications.
This is true for ADAS now, and it will also be true later when autonomous driving becomes a reality.
Medu Harsha is a Principal Application Engineer at Infineon Technologies.
Karthik Rangarajan is Senior Product Marketing Specialist at Infineon Technologies.