Interaction-Aware Driver Maneuver Inference in Highways Using Realistic Driver Models
| Autor: | David González, Christian Laugier, Jilles Steeve Dibangoye, Victor Romero-Cano |
|---|---|
| Přispěvatelé: | Sierra-Gonzalez, David, Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Universidad Autónoma de Occidente (UAO), Université de Lyon-Institut National des Sciences Appliquées (INSA), This work was supported by Toyota Motor Europe. |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Intelligent Vehicles
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] 050210 logistics & transportation 0209 industrial biotechnology Computer science 05 social sciences [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] Inference Maneuver inference ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Motion prediction Risk estimation [STAT.ML] Statistics [stat]/Machine Learning [stat.ML] [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 020901 industrial engineering & automation [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] 0502 economics and business [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Simulation Inverse Reinforcement Learning |
| Zdroj: | Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017) Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017), Oct 2017, Yokohama, Japan ITSC |
| Popis: | International audience; In this work, we address the problem of lane change maneuver prediction in highway scenarios using information from sensors and perception systems widely used in automated driving. Our prediction approach is twofold. First, a driver model learned from demonstrations via Inverse Reinforcement Learning is used to equip a host vehicle with the anticipatory behavior reasoning capability of common drivers. Second, inference on an interaction-aware augmented Switching State-Space Model allows the approach to account for the dynamic evidence observed. The use of a driver model that correctly balances the driving and risk-aversive preferences of a driver allows the computation of a planning-based maneuver prediction. Integrating this anticipatory prediction into the maneuver inference engine brings a degree of scene understanding into the estimate and leads to faster lane change detections compared to those obtained by relying on dynamics alone. The performance of the presented framework is evaluated using highway data collected with an instrumented vehicle. The combination of model-based maneuver prediction and filtering-based state and maneuver tracking is shown to outperform an Interacting Multiple Model filter in the detection of highway lane change maneuvers regarding accuracy, detection latency—by an average of 0.4 seconds—and false-positive rates. |
| Databáze: | OpenAIRE |
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