An Evolutionary Approach to General-Purpose Automated Speed and Lane Change Behavior

Hoel, Carl-Johan; Wahde, Mattias; Wolff, Krister

doi:10.1109/icmla.2017.00-70

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…However, the method presented in this paper requires no such hand crafted features, and instead uses the measured state, described in Table I, directly as input. Furthermore, the method in [10] achieved a similar performance when it comes to safety and average speed, but the number of necessary training episodes was between one and two orders of magnitude higher than for the method that was investigated in this study. Therefore, the new method is clearly advantageous compared to the previous one.…”

Section: Discussionmentioning

confidence: 70%

“…Traffic simulation 1) Highway case: A highway case was used as the main way to test the method presented in this paper. This case was similar to the one used in the previous study [10]. For completeness, it is summarized below.…”

Section: A Reference Modelmentioning

confidence: 88%

“…The values of the mentioned parameters are presented in Table V. Further details on the setup of the simulations, and how the speed trajectories were generated, are described in [10].…”

Section: A Reference Modelmentioning

confidence: 99%

“…Furthermore, with no tuning, the same method can be applied to a different setting, in this case driving on a road with oncoming traffic. Two important differences compared to our previous approach in [10] is that the method presented in this paper does not need any hand crafted features and that the training is significantly faster. Moreover, this paper introduces a novel way of using a convolutional neural network architecture by applying it to high level sensor data, representing interchangeable objects, which improves and speeds up the learning process.…”

Section: Introductionmentioning

confidence: 97%

“…For example, the ones mentioned above are designed for highway driving, but if a different case is considered, such as driving on a road with oncoming traffic, a completely different method is required. In an attempt to overcome this issue, we introduced a more general approach in [10]. This method is based on a genetic algorithm, which is used to automatically train a general-purpose driver model that can handle different cases.…”

Section: Introductionmentioning

confidence: 99%

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Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

Hoel

Wolff

Laine

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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199

102

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This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.

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Section: Discussionmentioning

confidence: 70%

Section: A Reference Modelmentioning

confidence: 88%

“…The values of the mentioned parameters are presented in Table V. Further details on the setup of the simulations, and how the speed trajectories were generated, are described in [10].…”

Section: A Reference Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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