What do you do if your autonomous vehicle's algorithms are not performing optimally?

Based on the low performance, we need to debug / analyze the algorithm's each step to get more insights. This will be very useful to take an action towards the performance improvement. Potential issues can be found through the initial diagnosis procedure.

My personal experience says - While diagnosing, never start with any presumption. I was developing a rover system and while testing we found that the path it takes is not what it supposed to. And the hardware team said that it might be because of that margin of error in motor's torque and we wasted few days fixing that part. But the real problem was with the GPS chip, there was an error in 5th decimal place of the value that was not significant as a single value but If we see it as a path that is updating with new value every second then a small deviation at any point will lead to a major deviation on the full path.

Diagnose Issue: This step involves a thorough examination of the autonomous vehicle's performance to identify the root cause of any suboptimal behavior. It might entail analyzing data logs, running simulations, and conducting real-world tests to understand when and where the algorithms are failing. For example, if the vehicle is experiencing difficulties with lane keeping, the diagnosis might reveal problems with sensor fusion, environmental perception, or decision-making algorithms.

Update Software: Keeping the software up to date is essential for ensuring that the vehicle benefits from the latest advancements, bug fixes, and optimizations. This step may involve working closely with software developers to deploy patches or updates that address specific issues identified during the diagnosis phase. Additionally, updating software can enhance the vehicle's capabilities, reliability, and safety features.

Calibration Check: Sensors play a critical role in providing the vehicle with information about its surroundings. However, if sensors are not properly calibrated, they may provide inaccurate or inconsistent data, leading to algorithmic errors. Conducting regular calibration checks ensures that sensors are correctly aligned and calibrated according to manufacturer specifications. This step is crucial for maintaining the accuracy and reliability of sensor data, especially in dynamic environments.

Algorithm Training: Autonomous vehicle algorithms, particularly those based on machine learning techniques, require continuous training and refinement to improve their performance. This step involves collecting and annotating relevant datasets, training the algorithms using various techniques such as supervised or reinforcement learning, and evaluating their performance on validation datasets. By iteratively training and fine-tuning the algorithms, developers can enhance their ability to accurately perceive and interpret complex driving scenarios.

Professional Help: Sometimes, resolving algorithmic issues may require expertise beyond the capabilities of an internal team. Seeking professional help from consultants, researchers, or industry experts can provide valuable insights and solutions to challenging problems. These professionals may offer specialized knowledge in areas such as computer vision, sensor fusion, motion planning, or control systems, helping to accelerate the resolution of algorithmic issues and improve the overall performance of the autonomous vehicle.

Safety First: Throughout the process of diagnosing and addressing algorithmic issues, safety should remain the top priority. Autonomous vehicles operate in dynamic and unpredictable environments, where even small errors can have significant consequences. Implementing robust safety measures, conducting thorough risk assessments, and adhering to industry best practices ensure that any modifications or updates to the algorithms do not compromise the safety of the vehicle or its occupants. Additionally, incorporating fail-safe mechanisms, redundant systems, and rigorous testing procedures helps mitigate risks and build trust in the autonomous vehicle's capabilities.

In the realm of autonomous driving, numerous unforeseen scenarios may arise beyond the anticipation of the driving software. In the event of any such occurrence, it is advisable to transition to manual mode and address the situation directly using the human intervention.

You could also ignore the issue, label it as "full self driving" or "autopilot", then put it into products and sell it. Then when your safety stats are going downhill, spring a load of restrictions onto the users who paid for said full self driving package.