| 1. |
- Aramrattana, Maytheewat, et al.
(författare)
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Evaluating Model Mismatch Impacting CACC Controllers in Mixed
- 2018
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Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538644522 - 9781538644515 - 9781538644539 ; , s. 1867-1872
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Konferensbidrag (refereegranskat)abstract
- 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 proof- of-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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| 2. |
- Aramrattana, Maytheewat, 1988-, et al.
(författare)
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Evaluating Model Mismatch Impacting CACC Controllers in Mixed Traffic using a Driving Simulator
- 2018
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Ingår i: 2018 IEEE Intelligent Vehicles Symposium (IV). - New York, NY : IEEE. - 9781538644522 - 9781538644515 - 9781538644539 ; , s. 1867-1872
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Konferensbidrag (refereegranskat)abstract
- 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 proof-of-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. © 2018 IEEE.
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| 3. |
- Patel, Raj-Haresh, 1991, et al.
(författare)
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Buffer-Aided Model Predictive Controller to Mitigate Model Mismatches and Localization Errors
- 2018
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 3:4, s. 501-510
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Tidskriftsartikel (refereegranskat)abstract
- 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 such as 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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