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BMW will demonstrate new driver assistance and automated control functions including 360-degree collision avoidance and fully-automated parking in multi-story parking garages at the upcoming Consumer Electronics Show (CES) 2015 in January.
The platform for 360-degree collision avoidance is secure position and environment recognition; the research vehicle is a BMW i3. Four advanced laser scanners record the environment and reliably identify impediments such as columns, for example in a multi-story parking garage. If the vehicle approaches a wall or a column too quickly, the system brakes automatically to prevent the threat of collision. The vehicle is brought to a standstill very precisely with centimeters to spare.
If the driver steers away from the obstacle or changes direction, the system releases the brakes. This system relieves the burden on the driver in an environment with poor visibility and makes a further contribution to enhanced safety and comfort. Like all BMW assistance systems, this research application can be overridden at any time by the driver.
The fully automated Remote Valet Parking Assistant in the BMW i3 research vehicle combines information from the laser scanners with the digital site plan of a building—such as a parking garage. If the driver uses the Smartwatch to activate the fully-automated Remote Valet Parking Assistant, the system will steer the vehicle independently through the levels, while the driver has already got out of the car.
The fully automated Remote Valet Parking Assistant recognizes the structural features of the car park and equally reliably steers round any obstacles that appear unexpectedly—such as incorrectly parked vehicles. Once the BMW i3 has arrived at the parking space, the vehicle locks itself and waits to be called by Smartwatch and voice command. The fully automated Remote Valet Parking Assistant then calculates the exact time until the driver arrives at the car park and starts up the BMW i3 so that it arrives at the car park exit at exactly the right time.
BMW has succeeded in achieving fully automated control of the vehicle by connecting up vehicle sensor systems and a digital site plan. This avoids dependence on the GPS signal, which is not at all precise in multi-story garages. Alongside the laser sensors, the research vehicle also has the processing units and necessary algorithms on board and this means it can determine its exact position in the car park, monitor the environment perfectly, and carry out independent and fully automated navigation. It is not necessary to provide car parks, for example, with complex infrastructure in order to allow cars to orientate and navigate around the area safely.
Ongoing work on autonomous driving. As early as October 2009, the BMW Group gave a highly automated demonstration of driving round the North Loop of the Nürburgring on an ideal line in the precursor research project BMW Track Trainer. Later on, the BMW Track Trainer developed by engineers from BMW Group Research and Engineering demonstrated its performance on the race tracks at Laguna Seca, Zandvoort and Valencia, and back home on the Hockenheimring and the Lausitzring. The researchers gathered some important practical experience under extreme conditions at these venues for vehicle control and positioning.
Additional important findings were also provided by the research project entitled BMW Emergency Stop Assistant. If the driver collapses, for example in a medical emergency such as a heart attack, the vehicle changes to highly automated mode and can steer safely to the side of the road and initiate an emergency call.
In the middle of 2011, a test vehicle from the BMW Group drove along the A9 motorway from Munich towards Nuremberg without any interventions from the driver. Since then, this research prototype has been consistently developed. The test vehicle brakes, accelerates and overtakes entirely independently. These interventions are carried out in response to the momentary traffic situation in a speed range from 0 to 130 km/h (81 mph) and in compliance with the highway code. BMW prototypes have now driven some 20,000 test kilometers (12,400 miles). The vehicle is equipped with sensor systems like lidar, radar, ultrasound and camera recording on all sides.
Since January 2013, the BMW Group has been working with international automobile supplier Continental with the aim of moving the project forward. The overarching goal of the research partnership is to lay the groundwork for highly automated drive functions up to the year 2020 and beyond.
Assistance systems increase safety and comfort in road traffic, although the degree of driver support varies. The highest level of automation is provided by fully automated assistance systems.
Drive functions are fully automated if they no longer need to be monitored by the driver. There is no longer even any need for the driver to be in the vehicle—as in the case of the fully automated Remote Valet Parking Assistant.
The precursors for fully automated driving are highly automated systems which do not need to be monitored continuously by the driver. They take over the linear steering (forward and reverse motion) and transverse steering (sideways motion with the steering wheel) of the vehicle.
In contrast to fully automated systems, partially automated systems take control of linear and transverse steering of the vehicle (e.g. Congestion Assistant), but they need to be monitored at all times by the driver.
Assisted systems (e.g. ACC) in turn only provide support for the driver in linear or transverse steering.
The 28% drop in the US average retail price of gasoline since 23 June may not have much effect on automobile travel, and in turn, gasoline consumption, according to an analysis by the US Energy Information Administration (EIA). Gasoline is a relatively inelastic product—i.e., changes in prices have little influence on demand—and has become more so over the past few decades.
Price elasticity measures the responsiveness of demand to changes in price. Almost all price elasticities are negative—i.e., an increase in price leads to lower demand, and vice versa. Air travel, especially for vacation, tends to be highly elastic: a 10% increase in the price of air travel leads to an even greater (more than 10%) decrease in the amount of air travel. Price changes have greater effects if the changes persist over time, as opposed to being temporary shocks.
Automobile travel in the United States is much less elastic, and its price elasticity has fallen in recent decades. The price elasticity of motor gasoline is currently estimated to be in the range of -0.02 to -0.04 in the short term—it takes a 25% to 50% decrease in the price of gasoline to raise automobile travel 1%.
In the mid 1990s, the price elasticity for gasoline was higher, around -0.08, meaning it only took a 12% decrease in the price of gasoline to raise automobile travel by 1%.
EIA’s Short-Term Energy Outlook (STEO) uses a price elasticity of -0.02 to estimate and forecast consumption of motor gasoline, while also considering anticipated changes in travel demand and fuel economy. The December STEO expects that gasoline prices in 2015 will be 23% lower than the 2014 average, and consumption in December will be virtually unchanged from year-earlier levels, as increased fuel economy balances out increases in vehicle miles traveled in response to lower prices and other factors.
Price elasticities can be difficult to interpret, as demand can change for reasons beyond changes in fuel price, including changes in other economic factors (e.g., income), demographics, driver behavior, vehicle fuel efficiency, and other structural factors. EIA suggests some possible explanations for the decline in gasoline price elasticity in recent decades include:
The slowing of per-capita vehicle miles traveled (VMT). After increasing for decades, VMT per capita slowed in the late 1990s and even declined in recent years.
The retirement of the baby boomer generation, because retirees tend to drive less than the working-age population.
Population migrations to urban area, as opposed to rural and suburban areas, because urban residents typically drive less.
Declines in licensing rates for teenagers, as young people delay or avoid getting their drivers’ permits and licenses.
The reduced share of household income devoted to motor gasoline expenses. As gasoline represents a smaller share of household expenditures, drivers may be less sensitive to fluctuations in price.