Toyota Research Institute Shows Off its Next-Gen Self-Driving Tech
【Summary】Toyota Research Institute is demonstrating its rapid progress in the development of automated driving technology and other projects for the first time to the investor community this week.
Toyota Research Institute (TRI) is demonstrating its rapid progress in the development of automated driving technology and other projects for the first time to the investor community this week.
"In the last few months, we have rapidly accelerated our pace in advancing Toyota's automated driving capabilities with a vision of saving lives, expanding access to mobility, and making driving more fun and convenient," said Dr. Gill Pratt, CEO of TRI. "Our research teams have also been evolving machine intelligence that can support further development of robots for in-home support of people."
Automated Driving Platform 2.1
Since unveiling its Platform 2.0 research vehicle in March 2017, TRI has quickly updated its automated driving technology. The next iteration, dubbed Platform 2.1, is being shown to the public for the first time, on a closed-course.
In addition to the creation of this innovative test platform, TRI has made strong advances in deep learning computer perception models, which allow the automated vehicle system to more accurately understand the vehicle surroundings, detecting objects and roadways, and better predict a safe driving route.
These new architectures are faster, more efficient, and more highly accurate than models currently in use. In addition to object detection, the models' prediction capabilities can also provide data about road elements, such as road signs and lane markings, to support the development of high quality maps, which are a key component of automated driving functionality.
LiDAR Supplied by Luminar
Platform 2.1 also expands TRI's portfolio of suppliers, incorporating a new high-fidelity LIDAR system provided by Luminar, which was announced today. This new LIDAR provides a longer sensing range, a much denser point cloud to better detect positions of three-dimensional objects, and a field of view that is the first to be dynamically configurable, which means that measurement points can be concentrated where sensing is needed most.
The new LIDAR is married to the existing sensing system for 360-degree coverage. TRI expects to source additional suppliers as disruptive technology becomes available in the future.
On Platform 2.1, TRI created a second vehicle control cockpit on the front passenger side with a fully operational drive-by-wire steering wheel and pedals for acceleration and braking. This setup allows the research team to probe effective methods of transferring vehicle control between the human driver and the autonomous system in a range of challenging scenarios. It also helps with development of machine learning algorithms that can learn from expert human drivers and provide coaching to novice drivers.
TRI has also designed a unified approach to showing the various states of autonomy in the vehicle, using a consistent UI across screens, colored lights and a tonal language that is tied into Guardian and Chauffeur. The institute is also experimenting with increasing a driver's situational awareness by showing a point cloud representation of everything the car "sees" on the multi-media screen in the center stack.
With its broad-based advances in hardware and software, Platform 2.1 is a research tool for concurrent testing of TRI's dual approaches to vehicle autonomy – Guardian and Chauffeur – using just a single technology stack.
Under Guardian, the human driver maintains vehicle control and the automated driving system operates in parallel, monitoring for potential crash situations and intervening to protect vehicle occupants when needed. Chauffeur is Toyota's version of SAE Level 4/5 autonomy where all vehicle occupants are passengers.
Both approaches use the same technology stack of sensors and cameras. This week's road test in Texas, which marks the first time the Guardian system has been demonstrated publicly, includes multiple test scenarios to demonstrate TRI's advances in both applications.
These include the ability of the Guardian system to detect distracted or drowsy driving in certain situations, and to take action if the driver does not react to turns in the road. In such a situation, the system first warns and then will intervene with braking and steering to safely follow the road's curvature.
Chauffeur test scenarios demonstrate the vehicle's ability to drive itself on a closed course, navigate around road obstacles, and make a safe lane change around an impediment in its path with another vehicle travelling at the same speed in the lane next to it.
In addition to real-world testing, TRI is using simulation to accurately and safely test engineering assumptions, and investors can experience automated driving test scenarios in a virtual simulator.
Robotics and AI
Additionally, TRI is pursuing new concepts for applying artificial intelligence inside a vehicle cabin, to keep occupants comfortable, safe and satisfied. The institute has created a simulator showing an in-car AI agent that can detect a driver's skeletal pose, head and gaze position and emotion to anticipate needs or potential driving impairments. For example, when the system detects the driver taking a drink and facial expressions which might indicate discomfort, the agent hypothesizes that the driver might be feeling warm and lower the air conditioning or roll down the windows. If the agent detects drowsiness, it might provide a verbal prompt in the cabin suggesting that the driver pull over for coffee or navigate the car to the nearest coffee shop.
So far, the majority of what is currently being tested and developed in the field of autonomous vehicles has been relatively easy because most driving is easy. When we need autonomy to help is where autonomous driving can be complex. This is the difficult challenge that TRI intends to address.
Originally from New Jersey, Eric is a automotive & technology reporter covering the high-tech industry in Silicon Valley. Eric has over 15 years of automotive experience and a bachelors degree in computer science. These skills, combined with technical writing and news reporting, allows him to fully understand and identify new and innovative technologies in the auto industry and beyond. He has worked at Uber on self-driving cars and as a technical writer, helping people to understand and work with technology. Outside of work, Eric likes to travel to new places, play guitar, and explore the outdoors.
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