Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction

Scheel, Oliver; Schwarz, Loren; Navab, Nassir; Tombari, Federico

doi:10.1109/icra.2018.8462838

Cited by 21 publications

(11 citation statements)

References 12 publications

(26 reference statements)

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“…[30] reviews related studies and proposes an action planning method to enable an autonomous car to merge into a roundabout. While there are many studies focusing on intersections and roundabouts, [16], [19]- [31]. almost all consider urban scenarios.…”

Section: Driveability Factorsmentioning

confidence: 99%

“…Once all the scenes are labeled safe or hazardous, another CNN model is trained to predict whether a new scene is safe or hazardous. A similar approach is used in [16], which uses a bi-directional RNN to classify scenes as safe or unsafe for performing a lane change. However, the limitation of such metrics is that they are defined purely from the model prediction outcome, which is highly model dependent.…”

Section: A Scene Driveabilitymentioning

confidence: 99%

“…Related usages of driveability include "driveability map" which is a map divided into cells categorized as driveable or non-driveable for motion planning [11]- [14], and "object driveability" which determines if an object can be driven over without causing damage to the vehicle [15]. The concept that is most similar to that considered in this paper is "scene driveability" proposed in two recent studies [4], [16]. Scene driveability measures how easy a scene is for an autonomous car to make full decisions on steering angles and accelerations or to perform a specific task such as lane changing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Guo¹,

Kurup²,

Shah³

2020

IEEE Trans. Intell. Transport. Syst.

130

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With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Therefore, we also perform a comparative overview of 45 public driving datasets that enable learning and publish this dataset index at https://sites.google.com/view/driveability-surveydatasets. Specifically, we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.

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Section: Driveability Factorsmentioning

confidence: 99%

Section: A Scene Driveabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Guo¹,

Kurup²,

Shah³

2020

IEEE Trans. Intell. Transport. Syst.

130

View full text Add to dashboard Cite

show abstract

“…The datasets we used are NGSIM I-80 and NGSIM US-101 from American Federal Highway Administration. They have been widely used for intelligent transportation systems and validation of prediction algorithms (e.g., [40,41]). The two datasets contain six subsets of fifteen-minute collected trajectories (denoting as (I), (II), • • • , (VI)), which are collected by vision-based highway monitoring systems.…”

Section: Datasetsmentioning

confidence: 99%

“…To verify the performance of our approach, we compared our approach with other state-of-the-art approaches, which include a model predictive control (MPC)-based approach [43], a Bayesian network-based approach [34], a recurrent neural network (RNN)-based approach [41], a HMM-based approach [44] and a rule-based approach [45].…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

A Dynamic Bayesian Network for Vehicle Maneuver Prediction in Highway Driving Scenarios: Framework and Verification

Dai

et al. 2019

Electronics

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Accurate maneuver prediction for surrounding vehicles enables intelligent vehicles to make safe and socially compliant decisions in advance, thus improving the safety and comfort of the driving. The main contribution of this paper is proposing a practical, high-performance, and low-cost maneuver-prediction approach for intelligent vehicles. Our approach is based on a dynamic Bayesian network, which exploits multiple predictive features, namely, historical states of predicting vehicles, road structures, as well as traffic interactions for inferring the probability of each maneuver. The paper also presents algorithms of feature extraction for the network. Our approach is verified on real traffic data in large-scale publicly available datasets. The results show that our approach can recognize the lane-change maneuvers with an F1 score of 80% and an advanced prediction time of 3.75 s, which greatly improves the performance on prediction compared to other baseline approaches.

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A Novel Network Architecture of Decision-Making for Self-Driving Vehicles Based on Long Short-Term Memory and Grasshopper Optimization Algorithm

Shi

Fan

et al. 2020

IEEE Access

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Long short-term memory network is one of the most important network architectures of decision-making for self-driving vehicles. Nevertheless, the decision-making accuracy of long short-term memory network is limited, the information of the surrounding vehicles is not taken into consideration, which is critical for the decision-making of the ego vehicle, and the classification capability of long shortterm memory network is poor. In this paper, a novel network architecture called improved long short-term memory network with support vector machine classifier optimized by grasshopper optimization algorithm (GOA-ImLSTM) is proposed. Three improvements are presented in GOA-ImLSTM. Firstly, to consider the information of the surrounding vehicles, a new network architecture, used to extract vital features for selfdriving vehicles, with three parallel long short-term memory network units and a network unit serial connected according to vehicle location is designed. Secondly, to improve classification accuracy, support vector machine with stronger classification capability than softmax is introduced to accomplish the classification task. Thirdly, to promote the classification capability of support vector machine, grasshopper optimization algorithm is employed to optimize the parameters of support vector machine. Moreover, to balance exploration and exploitation ability of grasshopper optimization algorithm, dynamic weights in position movement formula are defined. The experiments indicate that GOA-ImLSTM improves the accuracy of results compared with other decision-making methods for self-driving vehicles on the Next Generation SIMulation. INDEX TERMS grasshopper optimization algorithm, long short-term memory, self-driving decisionmaking, support vector machine.

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Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction

Cited by 21 publications

References 12 publications

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

A Dynamic Bayesian Network for Vehicle Maneuver Prediction in Highway Driving Scenarios: Framework and Verification

A Novel Network Architecture of Decision-Making for Self-Driving Vehicles Based on Long Short-Term Memory and Grasshopper Optimization Algorithm

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