An array of sensors ensures complete coverage of a vehicle’s local environment

By Alix Paultre, contributing

One of the biggest debates in society is over electric
vehicle (EV) development and deployment. Being a highly charged issue (no pun
intended), the argument spills over and impacts electronic design whereby the discussion
is not only mirrored but layered as engineers discuss all aspects of the issue,
not just those most apparent. The good thing about engineers is that the
arguments are based on reality and what can be accomplished.

Unfortunately, perception often defines reality, so the
engineering community is, in one sense, held hostage to public opinion when it
comes to the rate and/or success of EVs in the marketplace. However, one aspect
of next-generation vehicles that is not in dispute is that the car of the
future will be fully or partially autonomous, regardless of the motivational
force driving the wheels.


The autonomous vehicle
of the near future relies on a network of integrated high-performance sensors
and intelligence to control its navigation by sensing the direction in which
the vehicle is traveling, surrounding traffic locations, speed of the vehicle, and
the location of the vehicle in relation to everything around it. (Source:

No matter how an EV is powered to reach its destination,
advanced navigation systems are critical to successful and safe performance.
This is not only important for vehicles but, frankly, any remote autonomous
system whether based in water, on the ground, or in the air. Even advanced
augmented reality systems require precise and accurate location-determination
capabilities to properly function. Creating a navigation system that empowers
true autonomy demands extensive simulation and in-field testing with data
collection and analysis systems.


“Know thyself” is a mantra for people, but an autonomous
system must also have some sense of where (and sometimes when and how) it is in
real space. This awareness is created by using an array of sensors to tell the
device about its environment coupled to a logic system that can take that information
and turn it into usable information and guidance.

In systems in which human safety is not a critical issue, a
little slop in the information is OK. People playing Pokémon Go don’t have to worry about how accurate their
position information is, and some actually game the systems (pun intended),
using their phone’s wandering GPS to capture targets outside of their true
spatial location. This kind of
inaccuracy is obviously not tolerable when an error can result in an accident
costly in human lives as well as possible loss of the vehicle.

This is where sensor fusion takes place. A properly designed
autonomous system will use an array of sensors with overlapping and
complementary capabilities to ensure complete coverage of the vehicle’s local
environment. The more comprehensive a system’s awareness of its environment,
the more accurately it can determine its position and the path to its goal.

Sensor integration

There are several sensor types that must be considered when
creating a system to determine position and direction. First are “body” sensors
that precisely provide basic information like roll, pitch, and yaw. Once you
know how you are positioned, location is the next important thing.

GPS is a very good way to get basic positional information,
but it isn’t accurate enough for geopositioning without a secondary feedback to
“lock” the rough GPS data into the real world. When car-based GPS systems first
came out, several manufacturers put wheel sensors on the cars to track the
physical position referenced to local maps in memory.

This kind of sensing used to require a battery of sensors,
accelerometers, magnetometers, and gyroscopes that were all required to be used
in unison as they all have weaknesses that must be corrected by additional
sensor input.

Accelerometers can determine static roll and pitch like a
carpenter’s level, but the data will be wrong when accelerating or decelerating
because an accelerometer can’t distinguish gravity from linear acceleration on
its own. Magnetometers can determine a heading but cannot determine roll and
pitch, which is important to orientation. That is why you used to also need three
gyros and a filter to combine the information to get good roll, pitch, and
yaw/heading. Yaw can be achieved without magnetometers from gyros, but
gyroscopes will drift. A magnetometer in a car doesn’t work so well because of
all the metal in and around the vehicle, so you need info from a gyro and GPS.

This kind of brute-force solution using a battery of sensors
has now been supplanted by using the latest in integrated inertial sensing
modules to provide this important feedback.


CarSensors IMU Aceinna fig2

A single IMU device
(like an ACEINNA 380ZA-209) can replace the functions of a variety of other
sensors — all in a single component. (Source: ACEINNA)

A solution like ACEINNA’s
small-scale fully calibrated inertial measurement unit
(IMU) can serve demanding embedded applications that require a complete dynamic
measurement solution in a robust low-profile package. By integrating all of the
functions previously available only separately, an IMU not only saves space and
components but also development time and cost. To simplify connections to the
system, the IMU380ZA-209 provides a standard serial peripheral interface (SPI)
bus for cost-effective digital board-to-board communications.

The 9-axis IMU380ZA-209 can measure acceleration of up to 4g
and takes a supply voltage as low as 3 V and as high as 5.5 V. This wide
acceptance range eases system integration issues, reducing the amount of power
conversion electronics. A wide operating temperature from –40°C to 85°C ensures
that it will function in a variety of ambient environments, and a bandwidth
from 5 Hz to 50 Hz and a range of 200 degrees/second mean that it can handle
fast-changing situations.


IMU380Z Aceinna fig3

The ACEINNA 9-axis
IMU380ZA-200, measuring only 24.15
x 37.7 x 9.5 mm, can handle a wide range of operating temperatures and
requires a minimum amount of power. (Source: ACEINNA)

These performance aspects underscore the ability of an IMU
to not only reduce space and cost but also provide a higher level of
performance and reliability. By replacing the legacy suite of sensors needed to
perform the tasks with an integrated system using newer technology, you will
achieve a better result than that ensemble of sensors could do together.

Integration removes cables and wires, bulky connectors, and
separate housings, increasing robustness and reliability while integration of
functionality increases accuracy and precision. These advantages cascade
throughout the design from less complexity in the power electronics to improved
vehicle design from reduced and simplified internal space requirements.

Putting it together

When developing a typical design, dynamic trajectory and
modeled sensor readings (with errors) are simulated to create an Extended
Kalman Filter or Particle Filter navigation algorithm. Once a given simulation
shows promising results, that algorithm is implemented with real-time code on
embedded hardware. The resulting prototype is then tested in the field, where
data is logged (often into the terabytes) and then analyzed against predicted

To facilitate this process, developers of navigation systems
typically select and use different software for each step in this process.
Surprisingly, open-source “web technologies” can form the basis of a highly efficient
software stack for the development of navigation systems. As a real-world
example, ACEINNA has traditionally used a stack with many “classic” proprietary
embedded programming and simulation tools including Matlab for simulation, IAR
Systems for compiling embedded C/C++ code, and a combination of C# with
National Instruments tools to build configuration, graphing, and data logging
graphical user interfaces.

At the end of the day, all of this information is put into
large data files that are stored on in-house servers. At ACEINNA, the decision
was made to consider a new approach based on “web technologies.”

In one example, a web technologies-centric stack uses Python
for simulation, Microsoft’s open-source VS-Code, or GitHub’s Atom with Arm’s
GNU Compiler Collection (GCC) for an IDE and uses JavaScript to build user interface
and logging engines. Native user interfaces for desktop and mobile can even be
deployed with compatible projects such as Electron and React Native.

Cloud services such as Azure or AWS make it trivial to
catalog and manage large datasets collected from the field as well as share
those datasets both internally and externally to the organization. Both
JavaScript and Python are extremely well-supported by cloud vendors, allowing
trivial integration of their compute and storage services into the stack. The
license fees per developer are zero, and the libraries from numerical analysis
in Python to JavaScript-based graphing libraries are endless.

The biggest issue is the upfront work required to make the
embedded tool chain work. Professionally licensed tools can, at times, be more
user-friendly out of the box. However, the large number of web developers and
forums largely negate this advantage. With many specialized libraries targeted
to help embedded developers use open-source tools, the path is rapidly becoming
far easier to implement.

Driving forward

The creation of a truly autonomous vehicle that serves the
user safely and effectively must use a full palette of sensors integrated into
a system that is not only intelligent in its own right but also web-enabled to
leverage the external functionalities available to maximize system performance.
Proper systems integration can mean the difference between success and failure in
the highly competitive and unforgiving autonomous vehicle marketplace.