Expanding vision‐based ADAS for non‐structured environments

Antony, Joseph; Manikandan, S.

doi:10.1049/iet-its.2019.0530

Cited by 2 publications

(4 citation statements)

References 29 publications

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“…Once the prediction accuracy of this model is verified using NGSIM data set, the second data set which includes the traffic flow from Indian metro cities was applied. This data set is used in the work [4] and it includes all dynamics of non-structured environments. Also, this data set contains trajectories of 2 wheelers and 3 wheelers which are major constituents of multi road agent scenarios.…”

Section: Resultsmentioning

confidence: 99%

“…This data set contains 20446 road elements from about 31923 frames. The actual trajectory of each road element was recorded in the work presented in [4] and this trajectory information was compared against the results obtained with the proposed approach.…”

Section: Datasetmentioning

confidence: 99%

“…Also, ADAS systems face numerous challenges in urban traffic environments and with nonlane-based environments [3]. The characteristic differences between structured and non-structured environments have been explained in detail in [4]. The differences between lane-based traffic and non-lane based traffic can be observed with respect to the type of road users, driving discipline and movement directions.…”

Section: Introductionmentioning

confidence: 99%

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Surrounding Vehicle Motion Prediction in Non-Lane-Based Environments for ADAS Enhancement

Antony,

2023

IEEE Access

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The significance of forecasting the behavior of road agents is on the rise, particularly in Advanced Driver Assistance Systems (ADAS) enhancement. Predicting road agents' intentions holds paramount importance for Autonomous vehicles, especially considering the forthcoming coexistence of ADAS systems with heterogeneous road entities within urban roadways. The behavioral attributes of non-lane based traffic, featuring a mix of various elements, are prevalent not only in urban scenarios but also in unstructured environments. This research aims to predict the movement patterns of surrounding vehicles in non-lane based environments. This study captures the surrounding vehicles' intent to utilize tight lateral spaces in non-lane based environments through the variations of lateral descriptor values. The investigation takes into account several factors, including leveraging contextual cues, retaining spatial data related to neighboring vehicles, and identifying driving patterns. To achieve these objectives, a hybrid model is introduced, combining a modified structured Long Short Term Memory (LSTM) with lateral descriptor based uncertainty estimation on top of established detection and tracking algorithms. This integration enhances the ability to capture spatial attributes of neighboring vehicles along with the assessment of traffic conflict indicators with lateral descriptor contextual cues. The methodology is evaluated across two distinct datasets: one simulating scenarios of neighboring vehicles within well-defined urban road setups, and the other representing non-structured environments. The empirical findings highlight the effectiveness of the proposed method, showcasing an impressive 24.69% enhancement in prediction accuracy compared to baseline models with 5-seconds prediction horizon.

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Section: Resultsmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surrounding Vehicle Motion Prediction in Non-Lane-Based Environments for ADAS Enhancement

Antony,

2023

IEEE Access

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“…The main target of ADAS is to eliminate the human error in the driving process; consequently, this helps prevent or reduce the likelihood of crash occurrences. Through the perception of the surrounding environment of a vehicle via multiple sensors like Radars, Lidars, Ultrasonic sensors and cameras, ADAS are able to deliver assistance to the drivers while driving and warn them about risky situations that may lead to traffic accident [ 4 ] . Due to the advances recently made in camera technologies, vision-based ADAS have been and still an active research area because they have the potential to dramatically decrease the number of traffic accidents [4].…”

Section: Introductionmentioning

confidence: 99%

A Robust Lane Detection Method for Urban roads

Mohamed

Salama

Galal

et al. 2022

Journal of Advanced Engineering Trends

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Many intelligent transportation systems, such as advanced driver assistance systems (ADAS), have been being developed to increase traffic safety. ADAS have the potential to save lives and reduce crashes by eliminating the human error in the driving process. The Lane detection system is designed to identify and estimate the position of lane boundaries in front of the ego-vehicle. Thus, it serves as a crucial and fundamental component in various ADAS, for instance, lane keeping assistance and lane departure warning assistance systems. In this paper, a proposed method of visionbased lane detection in urban roads is presented. First, we define a region of interest to exclude the misleading parts of the road image, then a bird's-eye view of the road in front of the ego-vehicle is obtained by employing the inverse perspective mapping. Second, we utilize the distinct colors of lane markings to achieve robust lane markings candidate detection. Finally, the estimated lane boundaries are represented by quadratic models whose parameters are estimated from the detected lane pixels using the RANSAC algorithm. Furthermore, we present a thorough evaluation of the detection performance of the proposed method using the ground-truth data of the Caltech dataset and a comparative analysis between the quadratic model used in the proposed method and other models presented in the literature. Detection results show the effectiveness of the proposed method in detecting lane boundaries in different conditions in urban roads, including curved lanes, shadows, illumination variations and presence of street writings. Moreover, the overall process takes an average time of 30.63 milliseconds per frame.

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Expanding vision‐based ADAS for non‐structured environments

Cited by 2 publications

References 29 publications

Surrounding Vehicle Motion Prediction in Non-Lane-Based Environments for ADAS Enhancement

Surrounding Vehicle Motion Prediction in Non-Lane-Based Environments for ADAS Enhancement

A Robust Lane Detection Method for Urban roads

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