The ability to model and predict ego-vehicle's surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems. Acceleration prediction is important as one of the major components of traffic prediction. This paper proposes novel approaches to the acceleration prediction problem. By representing spatial relationships between vehicles with a graph model, we build a generalized acceleration prediction framework. This paper studies the effectiveness of proposed Graph Convolution Networks, which operate on graphs predicting the acceleration distribution for vehicles driving on highways. We further investigate prediction improvement through integrating of Recurrent Neural Networks to disentangle the temporal complexity inherent in the traffic data. Results from simulation with comprehensive performance metrics support that our proposed networks outperform state-of-the-art methods in generating realistic trajectories over a prediction horizon.
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Determining Region of Influence of Ego-Vehicle on Roadways for Vehicle Decision Making
Autonomous Vehicles (AVs) are an emerging and highly impactful technology on today's roads. When assessing the performance of AVs, it is useful to study their improvement relative to common metrics such as fuel economy/emissions, safety, and congestion. But metrics of the vehicle's performance alone may not be complete; an AV that is affecting and reacting to a smart traffic light, for example, may improve its own performance, but may cause the same intersection to degrade the performance of other vehicles around the AV. Similar concerns arise in nearly all AV topics: platooning, light pre-emption, lane tracking, etc. Thus, the assessment of the vehicle's impacts on surrounding traffic is important, possibly even more important than the improvements enabled on the AV alone. But what boundary, or factors, define the vehicles, equipment, etc. “surrounding” an AV?
The goal of this work is to characterize the boundary of vehicles “surrounding” an AV, referred to as Region of Influence, or ROI. Specifically, this work focuses on the problem that considering a perturbation is exerted into a traffic system, how far in time and space the perturbation from an AV’s decision can influence the surrounding system’s behavior. To achieve the goal, we utilized AIMSUN, a microscopic traffic simulator, to perform baseline and perturbed simulations. The ROI was evaluated by comparing trajectories of traffic surrounding
the ego vehicle using different metrics, including difference in trajectories, Euclidian distance, rate of change of Euclidian distance, total number of lane changes over the whole simulation space versus time and total number of lane changes over the whole simulation time versus distance to ego vehicle. The results show that the ROI can be viewed from different perspectives using these metrics, and it is dependent on speed variance of the traffic.
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- Award ID(s):
- 1932509
- NSF-PAR ID:
- 10395527
- Date Published:
- Journal Name:
- 2022 Road Safety and Simulation International Conference (RSS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Connected and automated vehicles (CAVs) extend urban traffic control from temporal to spatiotemporal by enabling the control of CAV trajectories. Most of the existing studies on CAV trajectory planning only consider longitudinal behaviors (i.e., in-lane driving), or assume that the lane changing can be done instantaneously. The resultant CAV trajectories are not realistic and cannot be executed at the vehicle level. The aim of this paper is to propose a full trajectory planning model that considers both in-lane driving and lane changing maneuvers. The trajectory generation problem is modeled as an optimization problem and the cost function considers multiple driving features including safety, efficiency, and comfort. Ten features are selected in the cost function to capture both in-lane driving and lane changing behaviors. One major challenge in generating a trajectory that reflects certain driving policies is to balance the weights of different features in the cost function. To address this challenge, it is proposed to optimize the weights of the cost function by imitation learning. Maximum entropy inverse reinforcement learning is applied to obtain the optimal weight for each feature and then CAV trajectories are generated with the learned weights. Experiments using the Next Generation Simulation (NGSIM) dataset show that the generated trajectory is very close to the original trajectory with regard to the Euclidean distance displacement, with a mean average error of less than 1 m. Meanwhile, the generated trajectories can maintain safety gaps with surrounding vehicles and have comparable fuel consumption.
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Accepted Manuscript1.0
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- The DOI is not currently available.
