The interaction between safety and security is a recurrent topic in our
discussions with NXP colleagues in both formal and informal settings. The
topic can become particularly messy when adding automotive and artificial
intelligence to the mix.
In a blog entry from last year—Future Challenges: Making Artificial Intelligence Safe—we mentioned that in NXP we’re preparing the terrain by setting strong
foundations, using the NXP whitepaper on morals of algorithms and Auto eIQ as
examples. We are not alone in our quest for safe and secure automotive AI. In
Germany, BSI and ZF, together with TuV Nord, are now trying to figure out how
to test AI in cars. The German Research Center for Artificial Intelligence
(DFKI) and TuV SUD are also looking at roadworthiness tests for AI systems.
Although it is not yet clear what these tests will entail, we continue
building on our foundation.
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How Future Vehicles Could Use ML/AI
On the inference side, we are convinced that future vehicles will use AI and
machine learning to provide safety-related functions. That is why we’ve
to investigate the effects of random hardware faults in AI-based systems. The
effort goes beyond what is state-of-the-art as established by
functional safety standards
(such as ISO 26262) since these have explicitly excluded machine learning from
Safety block diagram
One of the focus areas of this collaboration is fault injection. Fault
injection is a technique used to (1) verify the effectiveness of safety
mechanisms, (2) justify the robustness of a particular design to random
hardware faults and (3) verify in the field that a device can detect faults as
foreseen. Determining the link between the impact of random hardware faults at
the edge and machine learning models is key to establishing the trust
necessary to rely on them in the real world. Without trust, we will not be
able to use machine learning and AI for safety-related functions needed for
How Can Safety Be Used to Establish Trust?
Apropos of establishing trust, we understand that safety of occupants in a
vehicle that, in a near future, will be running an AI safety-related function
goes beyond flawless execution of the ML model. In this case, the focus shifts
from the hardware itself to the quality of the model.
It is common to perform an evaluation via a metric that relates to the average
performance of the model. It is typical to use accuracy to define the ratio of
correct classification. In object detection (which is necessary for radar) the
mAP is typically the metric of choice. In addition to the correct
classification of an object, the mAP also covers the quality of the derived
However, we believe that to have trust in a model, this is not sufficient.
These metrics do not distinguish strange mistakes from human errors that are
understandable, simply because an object has, for instance, a rare shape.
Furthermore, in case of a strange mistake, solving it requires knowledge on
what caused it. Also if a prediction is correct, the prediction may still be
based on an incorrect bias, which you want to prevent.
How to Understand AI Algorithms
In order to deal with these issues, we need methods to open the black box that
AI algorithms typically are and make the predictions understandable by humans.
An interesting example of such a method for neural networks is Grad-CAM. In a
paper published in 2019 by Ramprasaath R. Selvaraju et. al., the authors
explain how by multiplying feature maps with their gradients, we can determine
which parts of an input are most important for the model to come to its
prediction. This gives users a very valuable tool to better understand what a
model has learned in order to come to its predictions.
This creates more trust in the model and can help the developer to expose
shortcomings in the training set that can then be resolved.
As an example provided by the authors, two different models are trained to
identify doctors or nurses. Grad-CAM provides an overlay (similar to a heat
map) to tell a human which sections of a picture were used to make a decision.
In the first model (“biased”), the model uses facial characteristics to make
its choice. This is an unwanted situation, as it might decide for a “nurse”
given that the training set was biased (more pictures of female nurses and
more pictures of male doctors). After analysis and retraining, the second
model is now making decisions based on other elements. Grad-CAM is used to
verify that these elements are desired (such as a stethoscope).
This example can easily be extrapolated to safety-related functions. In the
case of driver monitoring systems detection, Grad-CAM could help ensure
unbiased training of driver fatigue markers.
To explore specialized training
along four different learning paths, visit our
NXP continues to invest in solutions for making AI safe and secure. By
establishing trust in training data and ensuring flawless execution of ML
models, even in case of random hardware faults, we work towards a bright, safe