Have you ever wondered about Advanced Driver Assistance Systems (ADAS) design best practices? What about which of those best practices could be drastically enhanced by using AI?
Let’s explore some slightly non-obvious best practices and then see if using AI could help.
Sensor Fusion Optimization
It’s possible to combine and integrate data from many different sensors to create a more accurate and realistic perception of the environment. The fusion process could be optimized to reduce latency and speed up the processing algorithms for real-time decision making. Sensors for cameras, LiDAR, radar, and ultrasonic could all be integrated to improve accurate decision making. It’s also possible to utilize redundant sensor systems to cross-validate data. This could easily reduce to likelihood of false positives or negatives, resulting in better object detection, lane keeping, and collision avoidance.
But what about AI? Can AI help with sensor fusion optimization? As a matter of fact, machine learning models can be trained to detect and interpret patterns within the data streams from cameras, LiDAR, radar, etc. AI can also dynamically adjust the weighting of sensor inputs based on environmental conditions, which is crucial for making real-time decisions.
Real-World Data Collection and Continuous Learning
If possible, try to collect data from a large number of vehicles – like a fleet of vehicles that are driven in a similar capacity – in order to continuously improve ADAS algorithms. Whenever data anomalies appear, if there are enough counter examples, they will negate the effect of one-off anomalies. It’s also important to ensure that all data collection complies with privacy laws and regulations.
How can AI help? When you have an enormous amount of data collected from real-world scenarios, especially from a fleet of vehicles, AI and ML are essential to process and analyze the data for patterns. The AI models themselves can learn from diverse scenarios, identify edge cases, and continuously improve the ADAS systems. Over time, new driving environments, scenarios, and challenges will arise. As the ADAS systems encounter these new situations, AI will allow the system to improve on its own and become more robust over time.
Of course, there are other best practices that would benefit from AI and ML as well.
Comprehensive Regulatory Compliance
It’s essential to stay ahead of the ever-changing regulatory standards by engaging with policy makers, industry groups, or subscribing to an AI platform like Humaxa that does all that work for you. This ensures that the ADAS solutions are not just compliance but designed specifically to adapt to new and changing regulations. It’s also critical to design with global regulations in mind, and to navigate to a common solution that adheres to compliance regulations across the board. Keep in mind that in emerging markets, regulations typically evolve very rapidly.
Cybersecurity Measures
Naturally, nobody wants their vehicles hacked. When implementing secure communication protocols, it’s critical to protect against hacking or tampering, especially in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Running regular security audits is also important so you can identify and mitigate vulnerabilities in the ADAS system, ensuring that updates and patches are rolled out promptly.
If you would like help in conquering any of these best practices, please connect – I’d be happy to help.
Carolyn Peer
CEO/Co-founder, Humaxa
carolyn.peer@humaxa.com
