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Around 1.3 million people die each year as a result of road traffic crashes,
according to the statistics from the World Health Organization (WHO).
It also indicated that road traffic crashes are expected to become the seventh leading cause of death by 2030.
Hence, worldwide governments are setting mandatory regulations which are
increasing the use of applications such as advanced driver assistance systems
(ADAS) to reduce the risk of traffic accidents.
ADAS uses devices such as cameras, radar, LIDAR and ultrasonic as sensors
to detect near and far fields in different directions to provide driving assistance.
Blind spots, traffic signs, obstacles and distances can be detected, ensuring safe
driving and assistance in parking. Also, the system can warn the driver in a timely manner to prevent traffic accidents or suggest alternative routes to avoid traffic.
The proliferation of ADAS is expected to boost the installation of sensors and
cameras.
However, this is also challenging systems to promptly understand and analyze the massive amounts of raw and unorganized data being collected in this dynamic
traffic environment.
ADAS can become a thinking machine with human-like intelligence.
It is indicated that machine learning can leverage vehicle sensors and map data
to provide context to the vehicles environment for better and more proactive
decisions.
Machine learning helps ADAS to detect and recognize objects with improved accuracy and reliability.
The technology is known for its excellent performance in image classification
that can distinguish objects like pedestrians, bicycles or vehicles.
Also, the machine learning algorithm can help ADAS recognize road signs
more precisely like speed limit recognition.
This is gaining traction in ADAS applications due to continuous developments
and improvements in machine learning algorithms.
Compared to older classical vision algorithms, the improvements allow video data
to be understood and interpreted at a much higher level of resolution.
“It leads to new applications where the areas around the vehicle can be classified
as ‘available for driving’ or ‘occupied by an obstacle
if a certain type’ more reliably, helping to prevent dangerous driving situations.”
It is expected that improvement can enable safer vehicle operation
at autonomous SAE levels 3 and higher on highway scenarios very soon.
emergency braking, blind spot detection and lane departure assistance are
currently based on traditional vision algorithms.
It is indicated, however, that machine learning is a more robust method that
is well suited for the harsh automotive environment and can deliver on the
performance needed in ADAS systems.
When asked about enhanced features from machine learning in ADAS,
It is indicated that these are primarily oriented towards improvements in accuracy
rather than brand new functionalities.
Automatic emergency braking systems already exist and are quite functional
today, but they can be improved to better recognize pedestrians, cyclists or
animals in more varied poses. Through the use of sensors and cameras,
ADAS can identify pedestrians, bicycles and vehicles accurately to prevent traffic
accidents.
ADAS is not just for monitoring the roads ahead, but also for monitoring drivers.
Drowsy and distracted driving are key reasons for car crashes.
To prevent this, inward-facing cameras can be used to track a driver’s gazing
direction and head position. If the system detects that the driver is not looking
straight ahead while driving, it will alert him to do so.
Sometimes, clues appear in a drivers’ behavior or biological patterns rather than in their face or eye movements.
Mitsubishi Electric uses machine learning algorithms to detect absent-mindedness and other cognitive distractions in drivers when their vehicles are traveling
straight. The company uses the algorithm to analyze time-series data, including
information about the vehicle like steering and driver’s heart rate and
facial orientation.
The technology can predict appropriate driver actions in real time by using
a combination of data on normal driving and time-series data on the actual
vehicle and driver.
When the technology find the driver’s action differ from the algorithm-based
prediction of appropriate driving action, the driver will be immediately alerted.
“ADAS technology will continue to differentiate automakers and influence buying
decisions.
Automakers need scalable technology that can enhance the customer experience
at all levels of car ownership from economy to premium cars.
It is critical to offer ADAS hardware and software technology at cost points that
allow the markets to serve most customers.”
“Automakers want to introduce more features that will take advantage of smart sensors found throughout the vehicle.
These smart sensors will need to utilize machine learning algorithms which are
developed on much larger and power hungry computing platforms.
When these systems will be deployed in high volumes, cost-effective and power
efficient solutions are needed.”
There has been a growing interest in installing ADAS into new cars around the
world to increase driving safety and comfort.
Machine learning is believed to enhance ADAS performance as a kind of
differentiator.
No matter what kind of technology is applied, it’s vital to keep roads safer and
reduce traffic accidents to ensure every driver and pedestrians go home safe.