Avoiding potholes and pitfalls on the road to autonomous vehicles
19 April 2016
At a dirt test track near the Georgia Institute of Technology campus, researchers monitor a scale-model autonomous car as it drifts around corners.
The autonomous car drifts round the corners at a blistering eight metres per second – equivalent to 90mph in a full-size vehicle. Pushing this car to its limits could help make full-size driverless vehicles more stable in risky road conditions.
This one-fifth-scale device is just one of many research efforts aimed at helping the autonomous vehicle revolution happen successfully and safely.
Self-driving cars are unquestionably coming, guided variously by radar, lidar, motion sensors, cameras, GPS, and plenty of on-board computation. Already, semi-autonomous prototypes are operating under controlled conditions in California, and speculation about future autonomy includes visions of commuters napping through drive-time, high-speed convoys of networked big-rigs and a huge drop in accidents as robotic vehicles take over from impaired and distracted humans.
Yet these are only visions, where generalisations rule and few facts are established. At Georgia Tech, research focuses on the elusive but critical details of this phenomenon, as investigators from disciplines as diverse as industrial systems, design, engineering, computing, and psychology are developing a roadmap to robotic vehicles.
Researchers at Georgia Tech generally agree that a long period of adjustment, including generations of semi-autonomous vehicles, will be needed to reach completely autonomous transport on a large scale. Estimates of the time required vary from a couple of decades to more than half a century.
“Fully autonomous transport will require absolutely reliable navigation systems, major changes in highway infrastructure, and traffic control that’s synched to the vehicle, plus new fuelling, insurance, financing, and manufacturing paradigms,” said Vivek Ghosal, a professor in Georgia Tech’s School of Economics, who studies the automotive industry. “Yes, we have prototypes, but the operationalising of autonomy is still far away.”
A four-level model of the vehicular-automation process is now widely accepted. Level one denotes today’s driver-dependent cars; level two involves intelligent cruise and lane control with some automatic braking; level three indicates semi-autonomous vehicles that drive themselves but cede control to a human when conditions demand; and level four means fully autonomous with no driver controls.
Researchers at Georgia Tech, focusing on the gritty details, have spotlighted a list of complications that include:
• Human-machine interaction issues.
• Costly highway infrastructure changes.
• Unpredictable traffic effects.
• Conflicts between self-driving and human-driven vehicles.
• Guidance system reliability concerns.
• Vehicle ownership, liability, and business model shifts.
• Potential for major changes to the urban landscape.
Read the complete feature on the Research Horizons website.