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A Maneuver-Based Threat Assessment Strategy for Collision Avoidance

SAE International Journal of Passenger Cars - Electronic and Electrical Systems

Beihang University, China-Weiwen Deng
General Motors LLC, USA-Jinsong Wang
  • Journal Article
  • 07-12-01-0003
Published 2019-08-22 by SAE International in United States
Advanced driver-assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of the driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This article presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modelled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models. Subsequently, time to collision (TTC) is calculated in a strategy of employing collision detection at every predicted trajectory instance. For this purpose, safe areas via bounding boxes are applied on every vehicle, and Separating Axis Theorem (SAT) is applied for collision prediction…
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Personalized Eco-Driving for Intelligent Electric Vehicles

Jilin University-Weiwen Deng, Jian Wu, Yaxin Li
Jilin University, ASCL-Bohua Sun, Rui He
Published 2018-08-07 by SAE International in United States
Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed. Finally, MyEco-ACC is constructed and optimized via theory of Nonlinear Model Prediction Control (NMPC). Regenerated energy is taken as the indicator for energy consumption and the key parameter in driving style model…
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Research on the Classification and Identification for Personalized Driving Styles

CATARC-Liang Xu
China FAW Co., Ltd.-Lei Fu
Published 2018-04-03 by SAE International in United States
Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively. Three physical quantities, the root mean square of vehicle acceleration, the time-to-start and the time gap of each driver, are extracted from test samples, and their mean and variance are used as clustering samples. Then driving styles are defined and classified into three categories via Particle Swarm Optimization Clustering (PSO-Clustering) algorithm. The…
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A Maneuver-Based Threat Assessment Strategy for Collision Avoidance

General Motors LLC-Jinsong Wang
Jilin University-Yaxin Li, Weiwen Deng, Bohua Sun, Jian Zhao
Published 2018-04-03 by SAE International in United States
Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models. Subsequently, time to collision (TTC) is calculated in a strategy of employing collision detection at every predicted trajectory instance. For this purpose, safe areas via bounding boxes are applied on every vehicle, and Separating Axis Theorem (SAT) is applied for collision prediction,…
Annotation ability available