Prediction-Uncertainty-Aware Decision-Making for Autonomous Vehicles

Yang, Kai; Wang, Hong; Wu, Jiahang; Qin, Yechen; Yu, Wenhao; Cao, Dongpu

doi:10.1109/tiv.2022.3188662

Cited by 86 publications

(23 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Related Workmentioning

confidence: 99%

“…The advantage of using data sets or the generated data by driving models is that it can reflect the driving diversity of the human driver, however, it is not easy to demonstrate the interaction between vehicles. [27,28] are some examples that use generated data or data sets for verification. The above various verification methods have their advantages and disadvantages, nonetheless, there are hardly any comprehensive verification experiments for a framework, including the verification of the prediction accuracy of dynamic obstacles, the verification of diverse driving behaviors in interactive dynamic scenarios, and the capacity to replan in emergencies, the real-time performance of the algorithm etc.…”

Section: Experimental Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Spatio‐temporal decision‐making and trajectory planning framework with flexible constraints in closed‐loop dynamic traffic

Zhang

Liu

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Driving in interactive dynamic traffic is a huge challenge for autonomous vehicles, especially for motion planning. The autonomous vehicle not only needs to predict the future states of the social vehicles to avoid a collision but also realizes the comfort and continuous plan. To deal with the problem, a spatio‐temporal decision‐making and motion planning framework with flexible constraints based on previous work is proposed. Improvements can be highlighted in three aspects. First, a neural network for trajectory prediction is trained and it is integrated into the framework, which can learn the social vehicles' reactions in the negotiation better and give more accurate predictions. Second, instead of setting the fixed local targets, the flexible‐constraint mechanism is introduced to increase the success rate of continuous planning, specifically, the local planning target is determined conditioned on the situation with an elastic factor. Third, two popular simulators with randomly disposed traffic participants are exploited, where vehicles are mutually influenced, more similar to real‐world traffic. The closed‐loop tests with zero crashes and the comparative experiments with traditional models demonstrate the superior performance and practical significance of the proposed framework.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Spatio‐temporal decision‐making and trajectory planning framework with flexible constraints in closed‐loop dynamic traffic

Zhang

Liu

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…The LSTM model is trained and evaluated on the highD [9] dataset, however, the planning algorithm is evaluated using two selected scenarios from the highD dataset. Tang et al [7] expanded upon this work by estimating the uncertainty of prediction using a deep ensemble technique. Then, the effects of uncertaintyaware prediction on motion planning are evaluated in a single cut-in and merging scenario.…”

Section: B Trajectory Prediction For Motion Planningmentioning

confidence: 99%

“…In addition, many existing trajectory prediction approaches lack a thorough analysis of how the learningbased prediction impacts downstream motion planning and control tasks. Even though some studies [5], [6], [7] offer qualitative analysis of motion planning using prediction data, they often fall short of providing a comprehensive comparison between learning-based predictions and conventional methods like the constant velocity prediction model. Additionally, these evaluation methods often neglect to adequately assess the influence of prediction quality in diverse driving conditions, such as high/low traffic densities.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios

Mozaffari

Arnold

Dianati

et al. 2021

Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems

View full text Add to dashboard Cite

Accurate trajectory prediction of nearby vehicles is crucial for the safe motion planning of automated vehicles in dynamic driving scenarios such as highway merging. Existing methods cannot initiate prediction for a vehicle unless observed for a fixed duration of two or more seconds. This prevents a fast reaction by the ego vehicle to vehicles that enter its perception range, thus creating safety concerns. Therefore, this paper proposes a novel transformer-based trajectory prediction approach, specifically trained to handle any observation length larger than one frame. We perform a comprehensive evaluation of the proposed method using two large-scale highway trajectory datasets, namely the highD and exiD. In addition, we study the impact of the proposed prediction approach on motion planning and control tasks using extensive merging scenarios from the exiD dataset. To the best of our knowledge, this marks the first instance where such a large-scale highway merging dataset has been employed for this purpose. The results demonstrate that the prediction model achieves state-ofthe-art performance on highD dataset and maintains lower prediction error w.r.t. the constant velocity across all observation lengths in exiD. Moreover, it significantly enhances safety, comfort, and efficiency in dense traffic scenarios, as compared to the constant velocity model.

show abstract

“…Zheng [14] has proposed a driving safety field (DSF) to model the risk factors according to the driver-vehicle-road relationships. Tang [15] applied APF to model the uncertainty of collision risk. To address the moving obstacles, researchers [15,16] have integrated APF with model predictive control (MPC) to generate predictive planning results.…”

Section: Introductionmentioning

confidence: 99%

Safe and Human‐Like Trajectory Planning of Self‐Driving Cars: A Constraint Imitative Method

Cui,

Hu,

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Safe and human‐like trajectory planning is crucial for self‐driving cars. While model‐based planning has demonstrated reliability, it is beneficial to incorporate human demonstrations and align the results with human behaviors. This work aims at bridging the gap between model‐based planning and driver imitation by proposing a constraint imitative trajectory planning method (CITP). CITP integrates artificial potential field and dynamic movement primitives, which have achieved both the ability to imitate human demonstrations as well as ensure safety constraints. During the planning process, CITP first encodes human demonstrations, local driving target, and traffic obstacles as attractive or repulsive effects, and then the trajectory planning problem is solved through model predictive optimization. To address the dynamics of traffic scenarios, a hierarchical planning strategy is proposed based on the division of planning process. CITP is designed with five modules, including LSTM‐based target generation, encoding attractive and repulsive effects with target, demonstrations and obstacles, and trajectory planning with model predictive optimization. Data collection and experiments are carried out based on CARLA driving simulator, and the effectiveness in terms of both safety and consistency with human behavior are reported.

show abstract

Prediction-Uncertainty-Aware Decision-Making for Autonomous Vehicles

Cited by 86 publications

References 39 publications

Spatio‐temporal decision‐making and trajectory planning framework with flexible constraints in closed‐loop dynamic traffic

Spatio‐temporal decision‐making and trajectory planning framework with flexible constraints in closed‐loop dynamic traffic

A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios

Safe and Human‐Like Trajectory Planning of Self‐Driving Cars: A Constraint Imitative Method

Contact Info

Product

Resources

About