Conference Paper

Machine learning based motion planning approach for intelligent vehicles

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... Motion planning consists of finding a path, searching for the safest maneuver, and determining the most feasible trajectory. For instance, the authors of [11] and [12] proposed approaches that are based on machine learning and neural networks for motion planning. However, the computation cost of the entire motion planning strategy can be reduced. ...
... Assume that the initial speed of the ego vehicle is 15 mph for this scenario. The cumulative rewards of all waypoints in a path are calculated using Equation (11), which are plotted in Fig. 13. The maximum reward that a path can achieve in this scenario is 43.58 and the minimum reward for this scenario is 19.91. ...
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Lane-change maneuver has always been a challenging task for both manual and autonomous driving, especially in an urban setting. In particular, the uncertainty in predicting the behavior of other vehicles on the road leads to indecisive actions while changing lanes, which, might result in traffic congestion and cause safety concerns. This paper analyzes the factors related to uncertainty such as speed range change and lane change so as to design a predictive Markov decision process for lane-change maneuver in the urban setting. A hidden Markov model is developed for modeling uncertainties of surrounding vehicles. The reward model uses the crash probabilities and the feasibility/distance to the goal as primary parameters. Numerical simulation and analysis of two traffic scenarios are completed to demonstrate the effectiveness of the proposed approach.
... Motion planning consists of finding a path, searching for the safest maneuver, and determining the most feasible trajectory. For instance, the authors of [11] and [12] proposed approaches that are based on machine learning and neural networks for motion planning. However, the computation cost of the entire motion planning strategy can be reduced. ...
... Assume that the initial speed of the ego vehicle is 15 mph for this scenario. The cumulative rewards of all waypoints in a path are calculated using Equation (11), which are plotted in Fig. 13. The maximum reward that a path can achieve in this scenario is 43.58 and the minimum reward for this scenario is 19.91. ...
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Full-text available
Lane-change maneuver has always been a challenging task for both manual and autonomous driving, especially in an urban setting. In particular, the uncertainty in predicting the behavior of other vehicles on the road leads to indecisive actions while changing lanes, which, might result in traffic congestion and cause safety concerns. This paper analyzes the factors related to uncertainty such as speed range change and lane change so as to design a predictive Markov decision process for lane-change maneuver in the urban setting. A hidden Markov model is developed for modeling uncertainties of surrounding vehicles. The reward model uses the crash probabilities and the feasibility/distance to the goal as primary parameters. Numerical simulation and analysis of two traffic scenarios are completed to demonstrate the effectiveness of the proposed approach.
... To obtain the curvature of the path, the centerline is equally divided into 6 segments and the positive and negative values of the curvature of each segment are integrated resulting in 12 discrete values (κ i p , κ i n ), with i ∈ {1, . . . , 6} [20]. ...
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