Enhancing ADAS performance for safe driving

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.

Enhancement from Machine Learning

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.
  

Driver State Monitoring

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.