Fuel economy testing of autonomous vehicles

Mersky, Avi Chaim; Samaras, Constantine

doi:10.1016/j.trc.2016.01.001

Cited by 135 publications

(64 citation statements)

References 20 publications

(46 reference statements)

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“…Páez et al (2012) found that people with disabilities who have used a car within the past 12 months are about 28% more likely to desire more leisure activities compared to those who have not (Páez and Farber, 2012) Many companies have announced plans to develop self-driving vehicles, and twelve companies have applied to test self-driving cars in California as of 2016 (Chew, 2016). Vehicle automation has the potential to greatly improve travel by reducing congestion, travel times, crashes, and potentially energy consumption (Anderson et al, 2014;Brown et al, 2014;Harper et al, 2016;Levin and Boyles, 2015;Mersky and Samaras, 2016;Wadud et al, 2016). The ability for smart vehicles to interact with smartphones and act as a taxi service to transport people to their destinations also serves as an advantage, reducing travel costs by almost 75 percent (Litman, 2013).…”

Section: Introductionmentioning

confidence: 99%

Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions

Harper

Hendrickson

Mangones

et al. 2016

Transportation Research Part C: Emerging Technologies

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347

168

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Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19-64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.

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Section: Introductionmentioning

confidence: 99%

Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions

Harper

Hendrickson

Mangones

et al. 2016

Transportation Research Part C: Emerging Technologies

Self Cite

347

168

View full text Add to dashboard Cite

show abstract

“…Due to potential benefits on traffic safety, driver productivity, road capacity, travel speed, energy consumption, and vehicular emission (Shladover et al, 2012;Greenblatt and Saxena, 2015;Levin and Boyles, 2016a,b;Mersky and Samaras, 2016), autonomous vehicles (AVs) have attracted tremendous attentions. Recent progress suggests AVs are on the horizon.…”

Section: Introductionmentioning

confidence: 99%

Optimal deployment of autonomous vehicle lanes with endogenous market penetration

Chen

Zhang

et al. 2016

Transportation Research Part C: Emerging Technologies

187

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“…With regard to cooperative driving behavior, it will be necessary to establish whether connectivity between vehicles will simply reduce latency times towards zero or will allow exchange of actionable intent information (cf. [53]), the latter of which extends beyond the protocols of the currently planned rollout of vehicle-tovehicle communications [54].…”

Section: Discussionmentioning

confidence: 99%

Protected Turning Movements of Noncooperative Automated Vehicles: Geometrics, Trajectories, and Saturation Flow

Liu

Lai

Kong

et al. 2018

Journal of Advanced Transportation

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This study is the first to quantify throughput (saturation flow) of noncooperative automated vehicles when performing turning maneuvers, which are critical bottlenecks in arterial road networks. We first develop a constrained optimization problem based on AVs' kinematic behavior during a protected signal phase which considers both ABS-enabled and wheels-locked braking, as well as avoiding encroaching into oncoming traffic or past the edge-of-receiving-lane. We analyze noncooperative ("defensive") behavior, in keeping with the Assured Clear Distance Ahead legal standard to which human drivers are held and AVs will likely also be for the foreseeable future. We demonstrate that, under plausible behavioral parameters, AVs appear likely to have positive impacts on throughput of turning traffic streams at intersections, in the range of +0.2% (under the most conservative circumstances) to +43% for a typical turning maneuver. We demonstrate that the primary mechanism of impact of turning radius is its effect on speed, which is likely to be constrained by passenger comfort. We show heterogeneous per-lane throughput in the case of "double turn lanes." Finally, we demonstrate limited sensitivity to crash-risk criterion, with a 4% difference arising from a change from 1 in 10,000 to 1 in 100,000,000. The paper concludes with a brief discussion of policy implications and future research needs.

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Fuel economy testing of autonomous vehicles

Cited by 135 publications

References 20 publications

Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions

Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions

Optimal deployment of autonomous vehicle lanes with endogenous market penetration

Protected Turning Movements of Noncooperative Automated Vehicles: Geometrics, Trajectories, and Saturation Flow

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