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Model predicts lane changes, could inform driver-assist systems

Green Car Congress

Researchers at the Nebraska Transportation Center have developed a new model to help predict when vehicles will change lanes. Their efforts could ultimately help give advanced driver-assistance systems more lead time to react. These categories are based on the result of the lane change.

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Model Predicts Lane Changes, Could Inform Driver-Assist Systems

EV Obssesion

A car in the right lane lingers beside the opening between your vehicle and the SUV directly ahead of you in the left. The post Model Predicts Lane Changes, Could Inform Driver-Assist Systems appeared first on EV Obsession.

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Audi’s work with HERE manifesting in enhanced new A8 software and services

Green Car Congress

It combines highly detailed 3D models of cities worldwide with correspondingly realistic representations of many buildings. The system even includes events far off the planned route if they could potentially have an impact on it. Information is permanently updated at frequent intervals.

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UR:BAN research project gives mid-term update on advanced driver assistance systems, connected vehicles

Green Car Congress

UR:BAN—User oriented assistance systems and network management—is developing advanced driver assistance and traffic management systems for cities. The research objectives are being pursued in three main thematic target areas: Cognitive Assistance; Networked Traffic System; and Human Factors in Traffic.

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VW ID. SPACE VIZZION electric concept makes global debut in LA

Green Car Congress

With an 82 kWh battery and a low drag coefficient of 0.24, the vehicle has a range of 590 kilometers (367 miles) on the WLTP cycle and a predicted range of up to 300 miles (483 km) on the EPA cycle. family models. The driver automatically and intuitively takes key information from the ID. SPACE VIZZION is 195.2

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Ford kicks off new automated driving research projects with MIT and Stanford University

Green Car Congress

The MIT research focuses on scenario planning to predict actions of other vehicles and pedestrians, while Stanford is exploring how a vehicle might maneuver to allow its sensors to peek around obstructions. First is the physical capability of the vehicle—how fast can it accelerate, brake, change direction laterally.

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CMU study shows autonomous vehicle algorithms can have considerable effect on fuel economy; need for new approaches in testing

Green Car Congress

Researchers in the College of Engineering at Carnegie Mellon University have determined that fuel efficiency for self-driving cars—within the bounds of current fuel economy testing—could improve by up to 10% under efficiency-focused control strategies when following another vehicle.