A collision mitigation strategy for intelligent vehicles to compensate for human factors affecting manually driven vehicles

Patel, Raj Haresh; Härri, Jérôme; Bonnet, Christian

doi:10.1109/itsc.2017.8317953

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…By using wireless vehicle-to-vehicle (V2V) communication, driver or autopilot vehicle may obtain signal (including distance, velocity, and acceleration) about distant vehicles beyond the line of sight, which has great potential for enhancing safety of vehicles [1], [2]. And research has demonstrated their applicability to improve mobility [3], [4] and fuel economy [5].…”

Section: Introductionmentioning

confidence: 99%

A Cruise Control Method for Connected Vehicle Systems Considering Side Vehicles Merging Behavior

et al. 2019

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On the basis of wireless vehicle-to-vehicle communication, this paper proposes a superior control strategy for the merging behavior of connected cruise control (CCC) considering the effect of time delays. A novel range policy that integrates the consideration of side-vehicle merging scene is established, based on which a general strategy is proposed focusing on the acceleration control and incorporating various information delays. In this portion, the time-delay effect for the plant stability and head-to-tail string stability of CCC is explored and analyzed in detail. The results demonstrate that the traffic efficiency, the driving safety, and the ride comfort of CCC vehicles can be improved by the proposed method. In addition, the sensitivity study regarding the controller gain and the platoon's connectivity structure is exploited, which is proved to be of importance for system stability. INDEX TERMS Connected cruise control (CCC), head-to-tail string, merging behavior, time delays.

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Section: Introductionmentioning

confidence: 99%

A Cruise Control Method for Connected Vehicle Systems Considering Side Vehicles Merging Behavior

et al. 2019

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“…In a mixed vehicle scenario, consisting of CACC and manually driven vehicles (MDVs), human factors like limited visibility and perception response time influence human's driving behavior [5] which can not be accurately predicted. Although online estimation and modeling of neighboring vehicles can be performed [6]- [8] it might be computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

Buffer-Aided Model Predictive Controller to Mitigate Model Mismatches and Localization Errors

Patel

Wymeersch

Härri

et al. 2018

IEEE Trans. Intell. Veh.

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Any vehicle needs to be aware of its localization, destination and neighboring vehicles' state information for collision free navigation. A centralized controller computes controls for Cooperative Adaptive Cruise Control (CACC) vehicles based on the assumed behavior of manually driven vehicles (MDVs) in a mixed vehicle scenario. The assumed behavior of the MDVs may be different from the actual behavior which gives rise to a model mismatch. The use of erroneous localization information can generate erroneous controls. The presence of a model mismatch and the use of erroneous controls could potentially result into collisions. A controller robust to issues like localization errors and model mismatches is thus required. This paper proposes a robust model predictive controller which accounts for localization errors and mitigates model mismatches. Future control values computed by the centralized controller are shared with CACC vehicles and are stored in a buffer. Due to large localization errors or model mismatches when control computations are infeasible, control values from the buffer are used. Simulation results show that the proposed robust controller with buffer can avoid almost the same number of collisions in a scenario impacted by localization errors as that in a scenario with no localization errors despite model mismatch.

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“…perception response time values are drawn from a normal distribution N (1.33, (0.27) 2 ) and are capped between 0.8 s and 1.8 s. Please refer to[15] for details on computation of perception response time for MDVs.…”

mentioning

confidence: 99%

Evaluating Model Mismatch Impacting CACC Controllers in Mixed Traffic Using a Driving Simulator

Aramrattana

Patel

Englund

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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At early market penetration, automated vehicles will share the road with legacy vehicles. For a safe transportation system, automated vehicle controllers therefore need to estimate the behavior of the legacy vehicles. However, mismatches between the estimated and real human behaviors can lead to inefficient control inputs, and even collisions in the worst case. In this paper, we propose a framework for evaluating the impact of model mismatch by interfacing a controller under test with a driving simulator. As a proofof-concept, an algorithm based on Model Predictive Control (MPC) is evaluated in a braking scenario. We show how model mismatch between estimated and real human behavior can lead to a decrease in avoided collisions by almost 46%, and an increase in discomfort by almost 91%. Model mismatch is therefore non-negligible and the proposed framework is a unique method to evaluate them.

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A collision mitigation strategy for intelligent vehicles to compensate for human factors affecting manually driven vehicles

Cited by 3 publications

References 10 publications

A Cruise Control Method for Connected Vehicle Systems Considering Side Vehicles Merging Behavior

A Cruise Control Method for Connected Vehicle Systems Considering Side Vehicles Merging Behavior

Buffer-Aided Model Predictive Controller to Mitigate Model Mismatches and Localization Errors

Evaluating Model Mismatch Impacting CACC Controllers in Mixed Traffic Using a Driving Simulator

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