Detection of Collision-Prone Vehicle Behavior at Intersections Using Siamese Interaction LSTM

Roy, Debaditya and Ishizaka, Tetsuhiro and Mohan, C. K. and et al, . (2022) Detection of Collision-Prone Vehicle Behavior at Intersections Using Siamese Interaction LSTM. IEEE Transactions on Intelligent Transportation Systems, 23 (4). pp. 3137-3147. ISSN 1524-9050

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As a large proportion of road accidents occur at intersections, monitoring traffic safety of intersections is important. Existing approaches are designed to investigate accidents in lane-based traffic. However, such approaches are not suitable in a lane-less mixed-traffic environment where vehicles often ply very close to each other. Hence, we propose an approach called Siamese Interaction Long Short-Term Memory network (SILSTM) to detect collision prone vehicle behavior. The SILSTM network learns the interaction trajectory of a vehicle that describes the interactions of a vehicle with its neighbors at an intersection. Among the hundreds of interactions for every vehicle, there maybe only some interactions that may be unsafe, and hence, a temporal attention layer is used in the SILSTM network. Furthermore, the comparison of interaction trajectories requires labeling the trajectories as either unsafe or safe, but such a distinction is highly subjective, especially in lane-less traffic. Hence, in this work, we compute the characteristics of interaction trajectories involved in accidents using the collision energy model. The interaction trajectories that match accident characteristics are labeled as unsafe while the rest are considered safe. Finally, there is no existing dataset that allows us to monitor a particular intersection for a long duration. Therefore, we introduce the SkyEye dataset that contains 1 hour of continuous aerial footage from each of the 4 chosen intersections in the city of Ahmedabad in India. A detailed evaluation of SILSTM on the SkyEye dataset shows that unsafe (collision-prone) interaction trajectories can be effectively detected at different intersections. © 2000-2011 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Mohan, C. K.
Item Type: Article
Uncontrolled Keywords: Driving behavior analysis; LSTM; Siamese networks; social force model; vehicle interaction analysis
Subjects: Physics > Mechanical and aerospace > Transportation Science & Technology
Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 13 Jul 2022 10:46
Last Modified: 13 Jul 2022 10:46
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