Vehicle trajectory prediction based on motion model and maneuver recognition

Houénou, Adam; Bonnifait, Philippe; Cherfaoui, Véronique; Yao, Wen

doi:10.1109/iros.2013.6696982

Cited by 348 publications

(199 citation statements)

References 12 publications

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“…Intent recognition is performed by mapping a set of continuous features into the discrete maneuvering space. In [13], the value of D is used to estimate the maneuvering intent of the vehicle. If the value is small, the vehicle is assumed to maintain its lane, whereas the vehicle is performing a lane change if the value is larger than a fixed threshold.…”

Section: Intent Estimationmentioning

confidence: 99%

Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

et al. 2017

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Existing collision avoidance methods for autonomous vehicles, which ignore the driving intent of detected vehicles, thus, cannot satisfy the requirements for autonomous driving in urban environments because of their high false detection rates of collisions with vehicles on winding roads and the missed detection rate of collisions with maneuvering vehicles. This study introduces an intent-estimation-and motion-model-based (IEMMB) method to address these disadvantages. First, a state vector is constructed by combining the road structure and the moving state of detected vehicles. A Gaussian mixture model is used to learn the maneuvering patterns of vehicles from collected data, and the patterns are used to estimate the driving intent of the detected vehicles. Then, a desirable long-term trajectory is obtained by weighting time and comfort. The long-term trajectory and the short-term trajectory, which are predicted using a constant yaw rate motion model, are fused to achieve an accurate trajectory. Finally, considering the moving state of the autonomous vehicle, collisions can be detected and avoided. Experiments have shown that the intent estimation method performed well, achieving an accuracy of 91.7% on straight roads and an accuracy of 90.5% on winding roads, which is much higher than that achieved by the method that ignores the road structure. The average collision detection distance is increased by more than 8 m. In addition, the maximum yaw rate and acceleration during an evasive maneuver are decreased, indicating an improvement in the driving comfort.

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Section: Intent Estimationmentioning

confidence: 99%

Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

et al. 2017

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“…Different approaches for computing the most probable trajectory of the leading vehicle already exist: by assuming constant yaw rate and acceleration (CYRA) [29] or by using a maneuver recognition module (MRM) [30]. Since MRM shows higher accuracy compared with CYRA for a longer time horizon prediction [30], the MRM approach is used in the following to generate the most probable trajectory of the leading vehicle.…”

Section: ➀ Optimal Trajectorymentioning

confidence: 99%

“…The prediction of the most likely trajectory is computed for each time t i within a given time horizon T h 1 . For more details, the reader is referred to [30]. Moreover, for each time t i , a polygon which represents the car occupancy is associated with each position of the computed trajectory.…”

Section: ➀ Optimal Trajectorymentioning

confidence: 99%

Fail-safe motion planning of autonomous vehicles

Magdici

Althoff

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-Formally verified methods for motion planning are required in order to guarantee safety for autonomous vehicles. In particular, we consider trajectory generation by considering the most probable trajectory of other traffic participants. However, if the surrounding vehicles perform unexpected maneuvers, a collision might be inevitable. In this paper, a fail-safe motion planner is developed, which generates optimal trajectories, yet guarantees safety at all times. Safety is achieved by maintaining an emergency maneuver which can safely bring the host vehicle to a stop while avoiding any collision. The emergency maneuver is computed by considering for a given time horizon the occupancy prediction which encloses all possible trajectories of the other traffic participants. The performance of the approach is evaluated through simulation against real traffic data.

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“…8 shows the prediction results of the three algorithms under different number of testing trajectories. We can see from the figure, the cost of TPMO algorithm is significantly more than DHMTP algorithm and Naive algorithm.…”

Section: Prediction Time Analysismentioning

confidence: 99%

“…How to accurately predict the location information of the driving vehicle is a difficult problem needed to be solved collectively [6]. There are already some research results, such as the clustering of moving objects, the anomaly detection, the location and the prediction of movement trend, where the driving vehicle position prediction technology is continuously improved, but because of the theory and technology of the immature, most models can't be well adapted to the needs of moving vehicle position prediction [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Vehicle trajectory prediction based on Hidden Markov Model

Ye¹,

Zhang²,

Wang³

et al. 2016

KSII TIIS

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In Intelligent Transportation Systems (ITS), logistics distribution and mobile e-commerce, the real-time, accurate and reliable vehicle trajectory prediction has significant application value. Vehicle trajectory prediction can not only provide accurate location-based services, but also can monitor and predict traffic situation in advance, and then further recommend the optimal route for users. In this paper, firstly, we mine the double layers of hidden states of vehicle historical trajectories, and then determine the parameters of HMM (hidden Markov model) by historical data. Secondly, we adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory. Finally, we propose a new algorithm (DHMTP) for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states, and predict the nearest neighbor unit of location information of the next k stages. The experimental results demonstrate that the prediction accuracy of the proposed algorithm is increased by 18.3% compared with TPMO algorithm and increased by 23.1% compared with Naive algorithm in aspect of predicting the next k phases' trajectories, especially when traffic flow is greater, such as this time from weekday morning to evening. Moreover, the time performance of DHMTP algorithm is also clearly improved compared with TPMO algorithm.

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Vehicle trajectory prediction based on motion model and maneuver recognition

Cited by 348 publications

References 12 publications

Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

Fail-safe motion planning of autonomous vehicles

Vehicle trajectory prediction based on Hidden Markov Model

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