| 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. |
- Tiger, Mattias, 1989-, et al.
(författare)
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Gaussian Process Based Motion Pattern Recognition with Sequential Local Models
- 2018
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Ingår i: 2018 IEEE Intelligent Vehicles Symposium (IV). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538644522 - 9781538644515 - 9781538644539 ; , s. 1143-1149
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Konferensbidrag (refereegranskat)abstract
- Conventional trajectory-based vehicular traffic analysis approaches work well in simple environments such as a single crossing but they do not scale to more structurally complex environments such as networks of interconnected crossings (e.g. urban road networks). Local trajectory models are necessary to cope with the multi-modality of such structures, which in turn introduces new challenges. These larger and more complex environments increase the occurrences of non-consistent lack of motion and self-overlaps in observed trajectories which impose further challenges. In this paper we consider the problem of motion pattern recognition in the setting of sequential local motion pattern models. That is, classifying sub-trajectories from observed trajectories in accordance with which motion pattern that best explains it. We introduce a Gaussian process (GP) based modeling approach which outperforms the state-of-the-art GP based motion pattern approaches at this task. We investigate the impact of varying local model overlap and the length of the observed trajectory trace on the classification quality. We further show that introducing a pre-processing step filtering out stops from the training data significantly improves the classification performance. The approach is evaluated using real GPS position data from city buses driving in urban areas for extended periods of time.
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| 4. |
- Bergman, Kristoffer, 1990-, et al.
(författare)
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Combining Homotopy Methods and Numerical Optimal Control to Solve Motion Planning Problems
- 2018
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Ingår i: Proceedings of the 29th IEEE Intelligent Vehicles Symposium. - : IEEE. - 9781538644515 ; , s. 347-354
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Konferensbidrag (refereegranskat)abstract
- This paper presents a systematic approach for computing local solutions to motion planning problems in non-convex environments using numerical optimal control techniques. It extends the range of use of state-of-the-art numerical optimal control tools to problem classes where these tools have previously not been applicable. Today these problems are typically solved using motion planners based on randomized or graph search. The general principle is to define a homotopy that transforms, or preferably relaxes, the original problem to an easily solved problem. In this work, it is shown that by combining a Sequential Quadratic Programming (SQP) method with a homotopy approach that gradually transforms the problem from a relaxed one to the original one, practically relevant locally optimal solutions to the motion planning problem can be computed. The approach is demonstrated in motion planning problems in challenging 2D and 3D environments, where the presented method significantly outperforms both a state-of-the-art numerical optimal control method and a state-of-the-art open-source optimizing sampling-based planner commonly used as benchmark.
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