Safer Cities: Autonomous vehicle startup accelerates development with deep learning

safer cities

The self-driving community in the valley looks like a mess of genius. There are numerous intertwined partnerships between startups and investors. Tech companies, mapmakers and automakers all intermix into the confusion.

Amid the chaos of interlinking collaborations, one brilliant team of engineers stands out with a promising edge.

In the self-driving race, Drive.AI has been generating a great deal of attention. Their team is uniquely composed of multiple deep-learning experts straight from Stanford University’s Artificial Intelligence Laboratory. Their scalable approach and aggressive pace makes their project designs quite promising.

Their claim to fame?

Drive is the only self-driving startup to birth designs with a thoroughly holistic use of deep learning technology. By focusing on humanized vehicle learning from the beginning, the future “magical world” of self-driving cars can be safe, friendly and reliable.

Deep learning simulates human brains

There’s a race in Silicon Valley to build self-driving cars. The mission is safer roadways. All the startups are gunning to hit Level 5 for full automation. But we’re not quite there yet.

Most of the tech teams are currently straining to hit Level 4 autonomy (mostly autonomous).

When it comes to creating effective self-driving cars, the biggest challenge has always been predicting the unpredictable. With a never-ending variety of potential road blocks, this has been especially difficult for technicians to account for. Researchers are working to gain excessive roadway data to better predict these obstacles.

With infinite potential road blocks, it’s crucial to develop safe predicting systems. Most self-driving groups are continuously increasing obstacle-avoiding algorithms based on newly understood obstacles and rules. They then program these new rules into the vehicle. This is a complicated process. It makes environmental learning a constant struggle.

Deep learning is better, and it’s had tremendous success in many nuanced situations. For instance, familiar machines such as Siri and Alexa have been able to master voice recognition. That’s a common example of deep learning responsiveness at work.

What’s unique about Drive is the way they have incorporated human learning abilities into the vehicle. They birthed their models with fully integrated deep learning use. This means that the machine learns its environment similar to the way humans process every day information.

The system starts to learn for itself and begins to create its own rules.

Humans learn more from familiarity recognition, while robots process new information using programmed algorithms. Think about it. If you show a child an image of a cat, that child will easily be able to identify it as such. We know it’s a cat because we’ve seen tons of cats before. We don’t have to break it down. We just know. That’s how human brains work. A robot would break down the cat photo piece by piece in order to identify it: two eyes, two ears, mammal, colored fur.

From day one Drive has incorporated human learning mechanisms into their design. They have been able to create what we call “responsive algorithms.”

This means the vehicle won’t have to be trained for every imaginable situation. It has the ability to recognized an old lady in a wheel chair and a child playing with a ball under the same category of pedestrians. This is possible because of the camera image understands the perceptual pattern. With that information, there are decision making and motion planning patterns as well.

Imagine a four-way stop where other drivers are supposed to follow specific rules. But sometimes they don’t. With situation-based deep learning, the vehicle can make a safe decision. This element of deep learning contrasts with the more traditional, rule-based systems.

Drive has designed their tech processes to make decisions more closely related to the way humans process new information. With this type of design, they’re able to scale it into all kinds of challenging environmental situations.

safer transport

Drive.AI startup moving fast

From your iPhone to your refrigerator, machines are getting smarter. With all this accepted tech progress, autonomous driving is still an incredibly new form of AI on the market.

The group of former lab mates believe that deep learning in autonomous vehicles is the future of transportation. Most self-driving groups will use deep learning to solve specific problems. However, drive started with deep learning as their basis of operation. Their complete integration goes against the traditional robotics approach.

Less than a year after they entered the public scene, Drive has sent a fleet of four nearly autonomous vehicles navigating through the streets of San Francisco Bay Area.

Their mission statement claims they’re “building the brain of self-driving cars.”

After attaining their Autonomous Vehicle Testing Permit, they’ve prioritized safety throughout all of their trial runs. By mid-February they released their first public video of their self-driving technology. You can clearly see their system navigating the roads in Mountain View, Calif.

The video displayed their test vehicles operating through rain, darkness, and even hail. The robots they’re building have been able to smoothly navigate these especially challenging weather conditions.

Drive works to develop systems that quickly interpret data from sensors and learn to control the vehicle’s behavior. More specifically they’re trying to classify the norms of driver and pedestrian behaviors. They anticipate that human behavior will change around self-driving cars.

Moving towards safer roadways

The end result of Drive’s mission is to give people hours and hours back of our time. We can enjoy living instead of driving.

The average American commuter will spend 42 hours stuck in traffic each year. All of this congestion leads to poor physical and mental states. Our vehicles add so many issues to our daily lives. That’s all outside of accidents. From air pollution and wasted time to sucking your income and making you fat, our current transportation is far less than ideal.

Self-driving vehicles can empower the disabled and the elderly. They can transform landscapes and cut back on CO2 emissions. They can even make cars fun again.

People have been dreaming about improved transportation for many decades. The self-driving race gets faster and faster, and each company wants to be first. News stories are full of confident press statements about grand visions and nearing completions.

When will our streets be filled with a majority of autonomous vehicles? With the brilliant engineers on board, we’ll continue to make progress. The predictions range from 2020 to 2040 for full integration of autonomous cars. We don’t know. But we hope that we will be prepared enough to see this beautiful world change.