2020SAE technical papers on CD-ROM/SAE technical paper seriesRequires access

Impact of Automated Lane Change Assist on Energy Consumption

Casey J. Troxler, Patrick N. Currier, Charles F. Reinholtz

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Abstract

This paper models adaptive cruise control combined with automated lane change assist to investigate the energy consumption improvements that such a system may provide compared to conventional adaptive cruise control. Automatically executing a lane change may improve efficiency, for example, when following a vehicle that is slowing to make a turn. Changing lanes while maintaining speed is hypothesized to be more efficient than staying in the same lane as the turning vehicle and reducing speed. The differences in such scenarios are simulated in a virtual environment using a cuboid model with idealized sensors. The ego-vehicle detects scenarios and performs a lane change to reduce or eliminate required speed changes. The results of the simulations compare the energy content of the resulting drive cycle as an idealized method to measure energy consumption for each cruise control strategy. The simulations consider traffic laws, such as turn signal requirements that may dictate the distance the ego-vehicle must travel before the lane changes can be executed. The results showed that energy consumption can be reduced with an automated lane change feature, but the benefits could be limited by sensor range and local law requirements.

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What this paper is about

This paper models adaptive cruise control combined with automated lane change assist to investigate the energy consumption improvements that such a system may provide compared to conventional adaptive cruise control. Automatically executing a lane change may improve efficiency, for example, when following a vehicle that is slowing to make a turn. Changing lanes while maintaining speed is hypothesized to be more efficient than staying in the same lane as the turning vehicle and reducing speed. The differences in such scenarios are simulated in a virtual environment using a cuboid model with idealized sensors. The ego-vehicle detects scenarios and performs a lane change to reduce or eliminate required speed changes. The results of the simulations compare the energy content of the resulting drive cycle as an idealized method to measure energy consumption for each cruise control strategy. The simulations consider traffic laws, such as turn signal requirements that may dictate the distance the ego-vehicle must travel before the lane changes can be executed. The results showed that energy consumption can be reduced with an automated lane change feature, but the benefits could be limited by sensor range and local law requirements.

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Available abstract

This paper models adaptive cruise control combined with automated lane change assist to investigate the energy consumption improvements that such a system may provide compared to conventional adaptive cruise control. Automatically executing a lane change may improve efficiency, for example, when following a vehicle that is slowing to make a turn. Changing lanes while maintaining speed is hypothesized to be more efficient than staying in the same lane as the turning vehicle and reducing speed. The differences in such scenarios are simulated in a virtual environment using a cuboid model with idealized sensors. The ego-vehicle detects scenarios and performs a lane change to reduce or eliminate required speed changes. The results of the simulations compare the energy content of the resulting drive cycle as an idealized method to measure energy consumption for each cruise control strategy. The simulations consider traffic laws, such as turn signal requirements that may dictate the distance the ego-vehicle must travel before the lane changes can be executed. The results showed that energy consumption can be reduced with an automated lane change feature, but the benefits could be limited by sensor range and local law requirements.

Key concepts: Energy consumption, Computer science, Consumption (sociology), Engineering, Electrical engineering, Social science, Sociology

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