Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration

Su, Jianyu; Beling, Peter A.; Guo, Rui; Han, Kyungtae

doi:10.1109/itsc45102.2020.9294294

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…First, we construct the BRS using a bicycle model for the ego vehicle and a unicycle model for the surrounding vehicle following [31], since these models are relatively more realistic than a point mass model. Second, we construct the FRS using a point mass model, mainly to accommodate control input predictors, which typically output two-dimensional acceleration values [30,32]. Details of the employed vehicle dynamics in this work are as follows.…”

Section: Vehicle Dynamics and Equivalent Transformation Between Brs A...mentioning

confidence: 99%

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Wang¹,

Li²,

Alonso–Mora³

et al. 2023

Preprint

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Risk assessment is a crucial component of collision warning and avoidance systems in intelligent vehicles. To accurately detect potential vehicle collisions, reachability-based formal approaches have been developed to ensure driving safety, but suffer from over-conservatism, potentially leading to false-positive risk events in complicated real-world applications. In this work, we combine two reachability analysis techniques, i.e., backward reachable set (BRS) and stochastic forward reachable set (FRS), and propose an integrated probabilistic collision detection framework in highway driving. Within the framework, we can firstly use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate a collision probability at each future time step. In doing so, the framework can not only identify non-risky events with guaranteed safety, but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles, and further incorporate confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments are conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data, and the efficiency and effectiveness of the framework with the infused confidence belief are tested both in naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising in real-world applications.

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Section: Vehicle Dynamics and Equivalent Transformation Between Brs A...mentioning

confidence: 99%

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Wang¹,

Li²,

Alonso–Mora³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Remarkably, Zhou et al (90) showcased the effectiveness of an RNNbased CF model in capturing the traffic oscillation characteristics, which provides an insight on including RNN (e.g., LSTM, GRU) in the deep neural network to retrofit the CF behavior in congested traffic condition. Moreover, some studies (91)(92)(93)(94) have demonstrated that by arranging the kinematic information of multiple neighbor vehicles in Laplacian-like feature matrices or tensors and applying graph convolution network to seize the inter-dependency and social pooling of data, performance in predicting the states of ego vehicles could be improved. This phenomenon indicates that features with higher dimension and organized in connected structure might lead to higher accuracy.…”

Section: Behavior Cloningmentioning

confidence: 99%

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Zhou

Laval

Zhou

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.

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“…Before comparing the performance of prediction models, we define several performance metrics as follows [9].…”

Section: B Performance Of Prediction Models 1) Performance Metricsmentioning

confidence: 99%

“…Longitudinal maneuvers are continuous, and usually a regressor is employed to map some features to a value of acceleration or deceleration. For example, acceleration could be predicted by a feedforward neural network model [7], a long short-term memory (LSTM) neural network model [8], and a graph convolution model [9]. Lateral maneuvers are discrete, which typically consist of three actions (i.e., left lane change, right lane change, and lane keeping).…”

Section: Introductionmentioning

confidence: 99%

“…• Extend the time-window labeling scheme by adding a time gap between positive samples and negative samples to decorrelate the features of positive samples and that of negative samples. • Propose an aggressive approach and a conservative approach to address the unstable prediction issue [9], and a comparison study is conducted between these two approaches. • Evaluate the performance of the proposed model by NGSIM dataset which is collected in a real-world traffic environment.…”

Section: Introductionmentioning

confidence: 99%

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Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron

Shou

Wang

Han

et al. 2020

2020 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

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Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent hazards in the surrounding environment. Many existing lane change prediction models take as input lateral or angle information and make short-term (< 5 seconds) maneuver predictions. In this study, we propose a longer-term (5~10 seconds) prediction model without any lateral or angle information. Three prediction models are introduced, including a logistic regression model, a multilayer perceptron (MLP) model, and a recurrent neural network (RNN) model, and their performances are compared by using the real-world NGSIM dataset. To properly label the trajectory data, this study proposes a new time-window labeling scheme by adding a time gap between positive and negative samples. Two approaches are also proposed to address the unstable prediction issue, where the aggressive approach propagates each positive prediction for certain seconds, while the conservative approach adopts a rollwindow average to smooth the prediction. Evaluation results show that the developed prediction model is able to capture 75% of real lane change maneuvers with an average advanced prediction time of 8.05 seconds.

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Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration

Cited by 11 publications

References 9 publications

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron

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