TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

Jiao, Ruochen; Zheng, Bin; Liang, Dave; Zhu, Qi

doi:10.1109/iros47612.2022.9981029

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…It includes modifying the training data or augmenting it with perturbations (Figure 1). These perturbations can be incorporated into the historical states X [7], [16], [18], [23], [38] and optionally into the future states Y [14], [24], [39], denoted as X and Ỹ . The objective is for the trained model to exhibit comparable behavior for perturbed and benign examples [40].…”

Section: B Robustness Improvement Strategiesmentioning

confidence: 99%

“…The task of predicting future trajectories of traffic participants is a complex time series regression problem [7]. It involves intricate contextual elements, including directional information, velocity, position, and time.…”

Section: Introductionmentioning

confidence: 99%

“…It involves intricate contextual elements, including directional information, velocity, position, and time. Furthermore, such trajectories inherently convey semantic and behavioral information, facilitating inference of the future movement of road users [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

Zhang

Sun

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or suboptimal behavior. To address these challenges, this paper presents a comprehensive framework that categorizes and assesses the definitions and strategies used in the literature on evaluating and improving the robustness of trajectory prediction models. This involves a detailed exploration of various approaches, including data slicing methods, perturbation techniques, model architecture changes, and post-training adjustments. In the literature, we see many promising methods for increasing robustness, which are necessary for safe and reliable autonomous driving.

show abstract

Section: B Robustness Improvement Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

Zhang

Sun

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

show abstract

“…Machine learning based techniques are increasingly popular in planning and decision making for autonomous driving [13,24,39], for their potential in improving average system performance under complex scenarios. The work in [30] proposes a concept of social perception, which inferences surrounding environment from other vehicles' reactions.…”

Section: Related Workmentioning

confidence: 99%

Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Jiao¹,

Zheng²,

Liang³

et al. 2023

Preprint

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Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However, it is expected that human-driven and autonomous vehicles, and connected and non-connected vehicles need to share the transportation network during the transition period to fully connected and automated transportation systems. Such mixed traffic scenarios significantly increase the complexity in analyzing system behavior and quantifying uncertainty for highly interactive scenarios, e.g., lane changing. It is even harder to ensure system safety when neural network based planners are leveraged to further improve efficiency. In this work, we propose a connectivity-enhanced neural network based lane changing planner. By cooperating with surrounding connected vehicles in dynamic environment, our proposed planner will adapt its planned trajectory according to the analysis of a safe evasion trajectory. We demonstrate the strength of our planner design in improving efficiency and ensuring safety in various mixed traffic scenarios with extensive simulations. We also analyze the system robustness when the communication or coordination is not perfect.

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“…Machine learning methods have natural strengths in interaction modeling and prediction [30], [31], [32]. [33] makes discrete behavior decisions for mandatory lane changing based on Bayes classifier and decision trees.…”

Section: B Interaction In Dense Trafficmentioning

confidence: 99%

Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic

Chen¹,

Li²,

Zhang³

et al. 2023

Preprint

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When the traffic stream is extremely congested and surrounding vehicles are not cooperative, the mandatory lane changing can be significantly difficult. In this work, we propose an interactive trajectory planner, which will firstly attempt to change lanes as long as safety is ensured. Based on receding horizon planning, the ego vehicle can abort or continue changing lanes according to surrounding vehicles' reactions. We demonstrate the performance of our planner in extensive simulations with eight surrounding vehicles, initial velocity ranging from 0.5 to 5 meters per second, and bumper to bumper gap ranging from 4 to 10 meters. The ego vehicle with our planner can change lanes safely and smoothly. The computation time of the planner at every step is within 10 milliseconds in most cases on a laptop with 1.8GHz Intel Core i7-10610U.

show abstract

TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

Cited by 13 publications

References 26 publications

On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic

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