Driving Behavior Assessment and Anomaly Detection for Intelligent Vehicles
| Autor: | Anshul Paigwar, Alessandro Renzaglia, Danwei Wang, Chule Yang, Christian Laugier |
|---|---|
| Přispěvatelé: | Nanyang Technological University [Singapour], 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), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Computer science business.industry 05 social sciences Real-time computing [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 02 engineering and technology Markov model [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Vehicle dynamics Acceleration 0502 economics and business 11. Sustainability Chaining 0202 electrical engineering electronic engineering information engineering Trajectory [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] 020201 artificial intelligence & image processing Anomaly detection Hidden Markov model business Risk management ComputingMilieux_MISCELLANEOUS |
| Zdroj: | CIS-RAM 2019-9th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) Robotics, Automation and Mechatronics (RAM) CIS-RAM 2019-9th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) Robotics, Automation and Mechatronics (RAM), Nov 2019, Bangkok, Thailand. pp.1-6 HAL CIS/RAM |
| Popis: | Ensuring safety of both traffic participants and passengers is an important challenge for rapidly growing autonomous vehicle technology. To this purpose, intelligent vehicles not only have to drive safe but must be able to safeguard itself from other abnormally driving vehicles and avoid potential collisions. Anomaly detection is one of the essential abilities in behavior analysis, which can be used to infer the moving intention of other vehicles and provide evidence for collision risk assessment. In this paper, we propose a behavior analysis method based on Hidden Markov Model (HMM) to assess the driving behavior of vehicles on the road and detect anomalous moments. The algorithm uses the real-time velocity and position of the surrounding vehicles provided by the Conditional Monte Carlo Dense Occupancy Tracker (CMCDOT) framework. Next, by associating with the road information, the movement of each vehicle can be classified into several observation states, namely, Approaching, Braking, Lane Changing, and Lane Keeping. Finally, by chaining these observation states using a Markov model, the abnormality of driving behavior can be inferred into Normal, Attention, and Risk. We perform experiments using CARLA simulator environment to simulate abnormal driving behaviors, and we provide results showing the successful detection of abnormal situations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|