Advanced Autonomy

This is Advanced Autonomy. I’m Luke Renner. You know, one of my favorite things about working in this space is that we are at the very, very beginning, and there are still tons of problems that autonomous vehicle researchers are still trying to solve. These problems span all areas of in-vehicle technology to wider infrastructure communication.

My guest today, Biao Ma, is the VP of Engineering and the Head of Autonomy at Cyngn. In this conversation, he’s going to give us an engineer’s insider look into the three main problems that autonomy has yet to solve: occlusion — which is seeing beyond blind spots — prediction, and the coordination of a networked vehicle fleet.

The Trolley Problem is an ethical dilemma common to the self-driving car space. Here’s how it works: There is a runaway trolley barreling down the railway tracks. Ahead, on the tracks, there are five people tied up and unable to move. The trolley is headed straight for them. You are standing some distance off in the train yard, next to a lever. If you pull this lever, the trolley will switch to a different set of tracks. However, you notice that there is one person on the side track. You have two options:

1. Do nothing and allow the trolley to kill the five people on the main track.
2. Pull the lever, diverting the trolley onto the side track where it will kill one person.

Which is the more ethical option? Or, more simply: What is the right thing to do?

My guest, Ben Landen, hates the trolley problem. He thinks it fails to capture the true ethical dilemmas that we find in this space — and makes a lot of false assumptions about how self-driving vehicles actually make decisions.

And so, that’s what we’re going to talk about today: the trolley problem, the problem with the trolley problem, and some alternative ethical dilemmas we should be asking instead.

Our guest today is Tim Varecka. Tim is the Director of Engineering and has been developing technology solutions for his entire career. Before joining Cyngn, he was in charge of new product development for Hexagon, an organization focused on the digital transformation of the mining industry. Now, at Cyngn, he takes his experience developing products for mining even further by helping mining operations integrate advanced autonomous systems into the vehicles they already own.

In this conversation, we’ll be talking about the intersection of autonomous vehicles and mining, diving into its unique challenges and possibilities — and exploring how AV technology development in this space is a far different animal.

Our guest today is Biao Ma. Biao is a VP of Engineering and the head of autonomy at Cyngn. He has spent his career in the autonomy space. He brings engineering expertise to the entire stack, including perception, planning & control, mapping & localization, and simulation.  He was a software architect for Baidu’s autonomous driving division and has a master’s degree in software engineering from Carnegie Mellon University.

In this conversation, we’ll be talking about the five significant changes that have come to the Autonomous Vehicle sector in the last five years. Biao defines “massive” as changes that either (a) didn’t exist five years ago or (b) have improved by many, many multiples.

My guest today is a colleague of mine here at Cyngn, Ben Landen. Ben is the Senior Director of Product and Partnerships and, if we’re being honest — my boss. Ben has been in the self-driving space for most of his career. He was head of product & business at DeepScale, a startup that built computer vision solutions using deep learning. DeepScale was acquired by Tesla in 2019. Prior to that, Ben ran an automotive semiconductor product line at Maxim Integrated. Hundreds of millions of his products are embedded in vehicles all over the world.