Name
Abstract | ||
|---|---|---|
During a lane change, vehicle collision warning systems detect the likelihood of collision and time to collision to warn vehicles of an imminent collision. In autonomous systems, a vehicle utilizes data obtained by its own sensors to predict future state of traffic. The data from on board sensors are limited by line of sight, measurement noise and motion parameters of the vehicle which affect the accuracy of prediction. Alternatively, in cooperative driving, vehicles transmit their parameters continuously. This is also beset by communication delays and message loss. To avoid these limitations, a vehicle should estimate its future state and broadcast it to others vehicles in the neighborhood. This necessitates vehicles to predict their future trajectories based entirely on its past. Since, low cost global positioning systems are becoming an integral part of vehicles; a vehicle knows its own position. This can be utilized by the vehicle for prediction of its future trajectory. In this paper, the effectiveness of lane change trajectory prediction on the basis of past positions is studied. The lane change trajectory of a vehicle is modeled as a time series and back propagation neural network is trained using real field data and its efficacy for short range and long range prediction is benchmarked. Simulation results using NGSIM data indicate that future lane change trajectory cannot be predicted with sufficient accuracy. The most important reason seemed to be the influence of other neighboring vehicles on the trajectory on the lane changing vehicle in addition to noise and complex dependence of future on the past values. The results also indicate that a vehicle changes its motion parameters during the entire lane change process. This confirms the active intervention of the driver in adjusting the trajectory on the basis of his assessment of the future state of its surrounding vehicles and entails the consideration of the state of surrounding vehicle for accurate prediction. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22619-9_7 | Transactions on Computational Science |
Keywords | Field | DocType |
neighboring vehicle,autonomous vehicle,vehicle collision warning system,motion parameter,neural network,entire lane change process,surrounding vehicle,future state,necessitates vehicle,future trajectory,vehicle utilizes data,lane change trajectory prediction,future lane change trajectory | Warning system,Broadcasting,Simulation,Computer science,Collision,Autonomous system (Internet),Global Positioning System,Line-of-sight,Artificial neural network,Trajectory | Journal |
Volume | Citations | PageRank |
13 | 2 | 0.45 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ranjeet Singh Tomar | 1 | 23 | 5.52 |
| Shekhar Verma | 2 | 199 | 36.10 |