Impact Of Adaptive Cruise Control Research Articles

An internal stochastic car-following model: Stochasticity analysis of mixed traffic environment

This paper investigates the impact of adaptive cruise control(ACC) vehicles on the stochasticity of human driving behavior by constructing a stochastic car-following model of human-driven vehicles (HDVs). Utilizing NGSIM dataset, the relationship between acceleration variance and space headway is analyzed, and a novel stochastic car-following model with headway is proposed to capture the internal stochasticity of drivers. Furthermore, the interaction between HDVs and AVs is explored by discussing stochasticity and stability in mixed traffic flow, using the proposed HDV model. The model parameters are calibrated based on NGSIM dataset and the simulation results indicate that the proposed stochastic car-following model can effectively reproduce the generation and propagation of traffic shocks without lane changes. Additionally, the simulations reveal that as the penetration rate of AVs increases in a lower range (0%–50%), the stochasticity of HDVs and stability in mixed traffic flow is substantially reduced. However, at higher penetration rates, increases in the AV penetration rate have a limited effect on the stochasticity of human driving behavior and the stability of mixed traffic flow. Concurrently, under conditions of low penetration rates, a smaller AV platoon size contributes more effectively to enhancing the stability of traffic flow and suppressing the stochastic behavior of HDVs. This research provides new insights for optimizing traffic flow control with automated vehicles.

The impact of Adaptive Cruise Control on the drivers’ workload and attention

The impact of Adaptive Cruise Control on the drivers’ workload and attention

Towards reducing the number of crashes during hurricane evacuation: Assessing the potential safety impact of adaptive cruise control systems

Ensuring safer mobility for evacuee drivers during a hurricane evacuation has always been a major concern for traffic managers. That concern has grown further, particularly after recent hurricanes, which forced millions of people to evacuate, causing significant congestion and a high number of traffic crashes. Though several strategies have been deployed to manage the heavy traffic demand during a hurricane evacuation, current approaches seem to have less impact on traffic safety. In a situation where people are ordered to evacuate to a safer place involving long hours of driving, perception related errors are inevitable. In such conditions, advanced driving assistance system or vehicle automation can have a positive impact. In this study, we assess the safety impact of Adaptive Cruise Control (ACC) systems during an evacuation. We develop a microscopic simulation model of evacuation traffic in SUMO and calibrate it using real-world traffic data collected during the evacuation period of hurricane Irma for a segment in the Interstate highway in Florida. To evaluate the safety impact of ACC systems, we adopt two surrogate measures: time to collision (TTC) and deceleration rate to avoid a collision (DRAC). Our simulation experiments show that, during the evacuation, about 49.7% of traffic collisions can be reduced at a 25% market penetration of ACC equipped vehicles. Our result has potential implications for hurricane evacuation management since a modest decrease in the number of crashes can help reduce the massive delays most commonly experienced during a major evacuation.

Impact of adaptive cruise control (ACC) system on fatality and injury reduction in China

Objectives The road traffic safety situation around the world is not optimistic. The development of intelligent vehicles has become an ideal way to reduce road traffic crashes. The adaptive cruise control (ACC) system is an effective intelligent vehicle active safety system for avoiding certain types of collisions. This study aimed to assess the safety benefits of ACC in China, including the potential maximum impact and realistic impact. Methods This study applies a national-level safety impact evaluation model to assess the safety benefits of ACC in China, including the potential maximum impact and realistic impact. Road traffic fatality and severe injury trends in China, proportion of different collision types in China, effectiveness of collision avoidance, and market penetration rate of ACC are considered in the potential maximum impact scenario. Furthermore, the ACC activation rate and the technology’s technical limitations, including its effectiveness in different weather, light, speed, and road conditions, are discussed in the realistic scenario. Results With a 100% market penetration rate, fatalities could be reduced by 5.48%, and injuries could be reduced by 4.91%. With a large increase in market penetration rate of ACC in the coming future, the reductions in fatalities and severe injuries are 324–957 and 1,035–2,737 in 2025 and 531–1,579 and 1,604–4,242 in 2030. Considering ACC’s activation rate and its 4 main limitations, the adjusted realistic result is approximately one-third of the potential maximum result. Conclusions The result clearly shows that the ACC system can improve road traffic safety in China. Technical limitations have a great impact on ACC’s safety benefits. Of all of the limiting factors, the turn-on rate provides the most room for improvement, and improving the suitability of the ACC system on curved and sloped roads provides the smallest effect.

The energy impact of adaptive cruise control in real-world highway multiple-car-following scenarios

BackgroundSurging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions.MethodsTractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives.ResultsOur data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%).ConclusionsIn general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.

Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways

Adaptive cruise control (ACC) has been considered one of the critical components of automated driving. ACC adjusts vehicle speeds automatically by measuring the status of the ego-vehicle and leading vehicle. Current commercial ACCs are designed to be comfortable and convenient driving systems. Little attention is paid to the safety impacts of ACC, especially in traffic oscillations when crash risks are the highest. The primary objective of this study was to evaluate the impacts of ACC parameter settings on rear-end collisions on freeways. First, the occurrence of a rear-end collision in a stop-and-go wave was analyzed. A car-following model in an integrated ACC was developed for a simulation analysis. The time-to-collision based factors were calculated as surrogate safety measures of the collision risk. We also evaluated different market penetration rates considering that the application of ACC will be a gradual process. The results showed that the safety impacts of ACC were largely affected by the parameters. Smaller time delays and larger time gaps improved safety performance, but inappropriate parameter settings increased the collision risks and caused traffic disturbances. A higher reduction of the collision risk was achieved as the ACC vehicle penetration rate increased, especially in the initial stage with penetration rates of less than 30%. This study also showed that in the initial stage, the combination of ACC and a variable speed limit achieved better safety improvements on congested freeways than each single technique.