The driverless algorithm was tested on vehicles that were one-fifth the size of their full-size counterparts

Driverless algorithm allows racing cars to maintain stability

Driverless vehicles have been shown to maintain control even as they manoeuvre around sharp corners at speed using a new algorithm coupled with on-board computers.

A Georgia Institute of Technology research team has developed the algorithm which could help make self-driving cars of the future safer under hazardous road conditions.

The new technology has been put to the test by racing, sliding, and jumping using a vehicle one-fifth the scale of fully autonomous auto-rally cars at the equivalent of 145kmph.

The algorithm and onboard computer are coupled with sensing devices which significantly increases the vehicle’s stability.

"An autonomous vehicle should be able to handle any condition, not just drive on the highway under normal conditions," said professor Panagiotis Tsiotras who worked on the project.

"One of our principal goals is to infuse some of the expert techniques of human drivers into the brains of these autonomous vehicles."

Traditional robotic-vehicle techniques use the same control approach whether a vehicle is driving normally or at the edge of roadway adhesion.

The Georgia Tech method, known as model predictive path integral control (MPPI), was developed specifically to address the non-linear dynamics involved in controlling a vehicle near its friction limits.

"Aggressive driving in a robotic vehicle - maneuvering at the edge - is a unique control problem involving a highly complex system," said project leader Evangelos Theodorou

"However, by merging statistical physics with control theory, and utilising leading-edge computation, we can create a new perspective, a new framework, for control of autonomous systems."

Using statistical methods, the team integrated large amounts of handling-related information, together with data on the dynamics of the vehicular system, to compute the most stable trajectories from myriad possibilities.

Processed by the high-power graphics processing unit that the vehicle carries, the algorithm continuously samples data coming from global positioning system (GPS) hardware, inertial motion sensors, and other sensors.

The onboard hardware-software system performs real-time analysis of a vast number of possible trajectories and relays optimal handling decisions to the vehicle moment by moment.

This approach combines both the planning and execution of optimized handling decisions into a single highly efficient phase.

It's regarded as the first technology to carry out this computationally demanding task, in the past, optimal-control data inputs could not be processed in real time.

The researchers' two auto-rally vehicles, which were custom built for the project, utilise special electric motors to achieve the right balance between weight and power.

Each vehicle has two forward-facing cameras, an inertial measurement unit, and a GPS receiver, along with sophisticated wheel-speed sensors.

The power, navigation, and computation equipment is housed in a rugged aluminum enclosure able to withstand violent rollovers.

One of the hurdles currently preventing driverless cars from becoming commercially available is concern from both consumers and governments about the technology’s safety.

A recent survey found that 70 per cent of Britons would not feel confident being a passenger in the first wave of driverless cars.

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