A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction

By Mohammad Bahram, Constantin Hubmann, Andreas Lawitzky, Michael Aeberhard, Dirk Wollherr. 2016

161 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Jacob Lambert
2. Alexander Carballo
3. Ekim Yurtsever
4. Nachiket Deo
5. Kazuya Takeda
6. Mohan Manubhai Trivedi
7. Jiatong Du
8. Zewei Zhou

In Control and Systems Engineering

1. Karen Leung
2. Marco Pavone
3. Kazuya Takeda
4. Mohan Manubhai Trivedi
5. Alexander Carballo
6. Edward Schmerling
7. Yizhou Wang
8. Wolf Vollprecht

In Safety, Risk, Reliability and Quality

1. Marco Pavone
2. Kazuya Takeda
3. Alexander Carballo
4. Ekim Yurtsever
5. Jacob Lambert
6. Mohan Manubhai Trivedi
7. Nachiket Deo
8. Derick A. Johnson

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “its. The proposed system tagged observations with predefined maneuvers. Then, the features of each type were learned in a data-centric manner with HMMs. Another learning based approach was proposed in [215], where a Bayesian network classifier was used to predict maneuvers of individual drivers on hig”
• Multi-Agent Tensor Fusion for Contextual Trajectory Prediction (2019, methodology): “Traditional methods for predicting or classifying trajectories model various kinds of interactions and constraints by hand-crafted features or cost functions [3, 5, 6, 8, 15, 22, 32].”
• Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction (2017, background): “, whether a driver intends to turn or continue straight at an intersection [10], or intends to initiate a highway lane change [11] without the notion of an evolving negotiation within the maneuver).”
• Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs (2018, methodology): “Many approaches [10], [16]–[18] also take into consideration the interaction between vehicles for assigning maneuver classes and predicting trajectories.”
• A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, methodology): “Next, BMW uses Dynamic Bayesian Networks to determine the driving intentions of surrounding vehicles and performs experiments on highways [204].”
• Convolutional Social Pooling for Vehicle Trajectory Prediction (2018, methodology): “The first set of approaches [2, 6] use hand crafted cost functions based on the relative configuration of vehicles and make optimal predictions of future motion with respect to these cost functions.”
• A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving (2017, methodology): “…Vehicle kinematics, Relations with road entities, Interactions with other vehicles no BN [11] Layered-HMM [15] Vehicle kinematics, Relations with road entities, Interactions with other vehicles, A priori knowledge no DBN [13] no Cost-map + BF [14] Yes HSS-HMM [17,18] Yes SMT + SLP [19] Appl.”