Human Intuition for Autonomous Vehicles

James Gowers, Vice President of Strategy & Business Development, Perceptive Automata joins Grayson Brulte on The Road To Autonomy Podcast to discuss developing human intuition for autonomous vehicles.

The conversation begins with James talking about leading the Harvard Business School soccer team to two National Championships and what he learned about teamwork as an Army Ranger in the German Federal Armed Forces.

Being part of a team, being a leader does not mean that you are autocratic. The more important way to lead is by influencing. Being a solid part of the team, carrying your weight, and leading by example.

– James Gowers

With a clear understanding of the rigors of leadership and a background in business from the Harvard Business School, Grayson asks James how he first joined the autonomous vehicle industry.

James shares a wonderful story of how a friendship with Mark Wheeler, Co-Founder & CTO of DeepMap led to him joining the industry. After a successful run at DeepMap, James joined Perceptive Automata to help them successfully raise their Series A round.

Getting from Series A to Series B is hard work.

– James Gowers

Perceptive Automata is working on developing human-like intuition for autonomous vehicles as driving is inherently a social activity. James goes onto explain how Perceptive Automata is developing machine-learning models that can glance at a human and make powerful predictions on their intent to cross the road.

Grayson asks James if the machine-learning models can learn situational awareness. For example, can the models learn if an individual is walking, wearing headphones to staring at their phones, and generally not paying attention to the surroundings? Yes. James explains how the models capture behavior.

Perceptive Automata’s approach to situational awareness caught the attention of Jim Adler, Founding Managing Director of Toyota AI Ventures which invested in the company’s Series A $16m round.

Along with the investment, Jim wrote a Medium blog post titled: “Predicting the World Around Autonomous Vehicles: Our Investment in Perceptive Automata” about “theory of mind” and why Toyota AI Ventures invested in Perceptive Automata.

In the Medium blog post, Jim wrote the following:

As I’ve said before, cars are “social.” They exist alongside other human-operated vehicles, cyclists, and pedestrians. When we’re behind the wheel, we constantly survey the roads looking for clues to help predict what other people will do. Will that teenage skateboarder jaywalk? Will the minivan driver speed up as I try to make an unprotected left-hand turn? Who goes first at a four-way stop if we all arrive at the same time?

People use a “theory of mind” to face those kind of split-second decisions all of the time. However, what comes relatively easily to us humans is incredibly difficult for autonomous vehicles. To improve safety for passengers and pedestrians alike, it is so important to have an intuitive self-driving system that is able to recognize, understand, and predict human behavior.

– Jim Adler, Founding Managing Director of Toyota AI Ventures

Jim’s Medium blog post summed up Perceptive Automata’s approach to situational awareness brilliantly. This approach is critical for autonomous vehicles which are deployed in dense urban environments.

Grayson and James go onto discuss prediction models and planning for scenarios such as a baseball game a European football game getting out. Creating a situation where large groups of individuals are pouring out on the sidewalks and the roadway.

Autonomous vehicles have to learn and be prepared for all situations. From large groups of individuals at sporting events to first-responder vehicles traveling down the road at high-speeds. Driving is unpredictable and human intuition is a critical part of driving safely.

Perceptive Automata is developing human intuition for autonomous vehicles to make the roads safer for both passengers in autonomous vehicles and pedestrians walking or riding bicycles.

Humans have this unique ability to glance at pedestrians and make, immediate, effortless predictions about someone’s intent based on social cues, body language, etc.

– James Gowers

This is exactly what Perceptive Automata is developing for autonomous vehicles. Grayson asks James if this technology could be deployed into the security industry to spot potential bad actors through behavior.

James explains how this technology can be applied to the security industry and the potential applications. Not only can Perceptive Automata’s technology be applied for security applications, but it can also be used in the retail business to predict intent. Will the consumer purchase this product? Do they like the color of the product? The potential applications for predicting human intuition are endless.

Wrapping up the conversation, Grayson and James discuss the current state of the autonomous vehicle industry and what happens if and when Apple unveils an electric autonomous vehicle with an AR (Augmented Reality) app store.

Subscribe to The Road To Autonomy on Apple Podcasts

Recorded on Friday, February 12, 2021.