Surround Vehicle Motion Prediction
DROCC: Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections
Presented by
Mushi Wang, Siyuan Qiu, Yan Yu
Introduction
This paper presents a surrounding vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). More specifically, it focused on the improvement of in-lane target recognition and achieving human-like acceleration decisions at multi-lane turn intersections by introducing the learning-based target motion predictor and prediction-based motion predictor. A data-driven approach for predicting the trajectory and velocity of surrounding vehicles on urban roads at multi-lane turn intersections is described. LSTM architecture, a specific kind of RNN capable of learning long-term dependencies, is designed to manage complex vehicle motions in multi-lane turn intersections. The results show that the forecaster improves the recognition time of the leading vehicle and contributes to the improvement of prediction ability
Previous Work
There are 3 main challenges to achieving fully autonomous driving on urban roads, which are scene awareness, inferring other drivers’ intentions, and predicting their future motions. Researchers are developing prediction algorithms that can simulate a driver’s intuition to improve safety when autonomous vehicles and human drivers drive together. To predict driver behavior on an urban road, there are 3 categories for the motion prediction model: (1) physics-based (2) maneuver-based; and (3) interaction-aware. Physics-based models are simple and direct, which only consider the states of prediction vehicles kinematically. The advantage is that it has minimal computational burden among the three types. However, it is impossible to consider the interactions between vehicles. Maneuver-based models consider the driver’s intention and classified them. By predicting the driver maneuver, the future trajectory can be predicted. Identifying similar behaviors in driving is able to infer different drivers' intentions which are stated to improve the prediction accuracy. However, it still an assistant to improve physics-based models. Recurrent Neural Network (RNN) is a type of approach proposed to infer driver intention in this paper. Interaction-aware models can reflect interactions between surrounding vehicles, and predict future motions of detected vehicles simultaneously as a scene. While the prediction algorithm is more complex in computation which is often used in offline simulations. As Schulz et al. indicate, interaction models are very difficult to create as "predicting complete trajectories at once is challenging, as one needs to account for multiple hypotheses and long-term interactions between multiple agents" [6].
Motivation
Research results indicate that less research is focused on predicting the trajectory of intersections. Moreover, public data sets for analyzing driver behavior at intersections are not enough, and these data sets are not easy to collect. A model is needed to predict the various movements of the target around a multi-lane turning intersection. It is very necessary to design a motion predictor that can be used for real-time traffic.
Framework
The LSTM-RNN-based motion predictor comprises three parts: (1) a data encoder; (2) an LSTM-based RNN; and (3) a data decoder. depicts the architecture of the surrounding target trajectory predictor. The proposed architecture uses a perception algorithm to estimate the state of surrounding vehicles, which relies on six scanners. The output predicts the state of the surrounding vehicles and is used to determine the expected longitudinal acceleration in the actual traffic at the intersection.
LSTM-RNN based motion predictor
Data
The real dataset is captured on urban roads in Seoul. The training model is generated from 484 tracks collected when driving through intersections in real traffic. The previous and subsequent states of a vehicle at a particular time can be extracted. After post-processing, the collected data, a total of 16,660 data samples were generated, including 11,662 training data samples, and 4,998 evaluation data samples.
Motion predictor
This article proposes a data-driven method to predict the future movement of surrounding vehicles based on their previous movement. The motion predictor based on the LSTM-RNN architecture in this work only uses information collected from sensors on autonomous vehicles, as shown in the figure below. The contribution of the network architecture of this study is that the future state of the target vehicle is used as the input feature for predicting the field of view.
Network architecture
A RNN is an artificial neural network, suitable for use with sequential data. It can also be used for time-series data, where the pattern of the data depends on the time flow. Also, it can contain feedback loops that allow activations to flow alternately in the loop. An LSTM avoids the problem of vanishing gradients by making errors flow backward without a limit on the number of virtual layers. This property prevents errors from increasing or declining over time, which can make the network train improperly. The figure below shows the various layers of the LSTM-RNN and the number of units in each layer. This structure is determined by comparing the accuracy of 72 RNNs, which consist of a combination of four input sets and 18 network configurations.
Input and output features
In order to apply the motion predictor to the AV in motion, the speed of the data collection vehicle is added to the input sequence. The input sequence consists of relative X/Y position, relative heading angle, speed of surrounding target vehicles, and speed of data collection vehicles. The output sequence is the same as the input sequence, such as relative position, heading and speed.
Encoder and decoder
In this study, the authors introduced an encoder and decoder that process the input from the sensor and the output from the RNN, respectively. The encoder normalizes each component of the input data to rescale the data to mean 0 and standard deviation 1, while the decoder denormalizes the output data to use the same parameters as in the encoder to scale it back to the actual unit.
Sequence length
The sequence length of RNN input and output is another important factor to improve prediction performance. In this study, 5, 10, 15, 20, 25, and 30 steps of 100 millisecond sampling times were compared, and 15 steps showed relatively accurate results, even among candidates The observation time is very short.
Motion planning based on surrounding vehicle motion prediction
In daily driving, experienced drivers will predict possible risks based on observations of surrounding vehicles, and ensure safety by changing behaviors before the risks occur. In order to achieve a human-like motion plan, based on the model predictive control (MPC) method, a prediction-based motion planner for autonomous vehicles is designed, which takes into account the driver’s future behavior. The cost function of the motion planner is determined as follows: \begin{equation*} \begin{split} J = & \sum_{k=1}^{N_p} (x(k|t) - x_{ref}(k|t)^T) Q(x(k|t) - x_{ref}(k|t)) +\\ & R \sum_{k=0}^{N_p-1} u(k|t)^2 + R_{\Delta \mu}\sum_{k=0}^{N_p-2} (u(k+1|t) - u(k|t))^2 \end{split} \end{equation*} where [math]\displaystyle{ k }[/math] and [math]\displaystyle{ t }[/math] are the prediction step index and time index, respectively; [math]\displaystyle{ x(k|t) }[/math] and [math]\displaystyle{ x_{ref} (k|t) }[/math] are the states and reference of the MPC problem, respectively; [math]\displaystyle{ x(k|t) }[/math] is composed of travel distance px and longitudinal velocity vx; [math]\displaystyle{ x_{ref} (k|t) }[/math] consists of reference travel distance [math]\displaystyle{ p_{x,ref} }[/math] and reference longitudinal velocity [math]\displaystyle{ v_{x,ref} }[/math] ; [math]\displaystyle{ u(k|t) }[/math] is the control input, which is the longitudinal acceleration command; [math]\displaystyle{ N_p }[/math] is the prediction horizon; and Q, R, and [math]\displaystyle{ R_{\Delta \mu} }[/math] are the weight matrices for states, input, and input derivative, respectively, and these weight matrices were tuned to obtain control inputs from the proposed controller that were as similar as possible to those of human-driven vehicles. The constraints of the control input are defined as follows: \begin{equation*} \begin{split} &\mu_{min} \leq \mu(k|t) \leq \mu_{max} \\ &||\mu(k+1|t) - \mu(k|t)|| \leq S \end{split} \end{equation*} Determine the position and speed boundary based on the predicted state: \begin{equation*} \begin{split} & p_{x,max}(k|t) = p_{x,tar}(k|t) - c_{des}(k|t) \quad p_{x,min}(k|t) = 0 \\ & v_{x,max}(k|t) = min(v_{x,ret}(k|t), v_{x,limit}) \quad v_{x,min}(k|t) = 0 \end{split} \end{equation*} Where [math]\displaystyle{ v_{x, limit} }[/math] are the speed limits of the target vehicle.
Prediction performance analysis and application to motion planning
Accuracy analysis
The proposed algorithm was compared with the results from three base algorithms, a path-following model with constant velocity, a path-following model with traffic flow and a CTRV model.
We compare those algorithms according to four sorts of errors, The [math]\displaystyle{ x }[/math] position error [math]\displaystyle{ e_{x,T_p} }[/math], [math]\displaystyle{ y }[/math] position error [math]\displaystyle{ e_{y,T_p} }[/math], heading error [math]\displaystyle{ e_{\theta,T_p} }[/math], and velocity error [math]\displaystyle{ e_{v,T_p} }[/math] where [math]\displaystyle{ T_p }[/math] denotes time [math]\displaystyle{ p }[/math]. These four errors are defined as follows:
\begin{equation*} \begin{split} e_{x,Tp}=& p_{x,Tp} -\hat {p}_{x,Tp}\\ e_{y,Tp}=& p_{y,Tp} -\hat {p}_{y,Tp}\\ e_{\theta,Tp}=& \theta _{Tp} -\hat {\theta }_{Tp}\\ e_{v,Tp}=& v_{Tp} -\hat {v}_{Tp} \end{split} \end{equation*}
The proposed model shows significantly fewer prediction errors compare to the based algorithms in terms of mean, standard deviation(STD), and root mean square error(RMSE). Meanwhile, the proposed model exhibits a bell-shaped curve with a close to zero mean, which indicates that the proposed algorithm's prediction of human divers' intensions are relatively precise. On the other hand, [math]\displaystyle{ e_{x,T_p} }[/math], [math]\displaystyle{ e_{y,T_p} }[/math], [math]\displaystyle{ e_{v,T_p} }[/math] are bounded within reasonable levels. For instant, the three-sigma range of [math]\displaystyle{ e_{y,T_p} }[/math] is within the width of a lane. Therefore, the proposed algorithm can be precise and maintain safety simultaneously.
Motion planning application
Case study of a multi-lane left turn scenario
The proposed method mimics a human driver better, by simulating a human driver's decision-making process. In a multi-lane left turn scenario, the proposed algorithm correctly predicted the trajectory of a target vehicle, even when the target vehicle was not following the intersection guideline.
Statistical analysis of motion planning application results
The data is analyzed from two perspectives, the time to recognize the in-lane target and the similarity to human driver commands. In most of cases, the proposed algorithm detects the in-line target no late than based algorithm. In addition, the proposed algorithm only recognized cases later than the base algorithm did when the surrounding target vehicles first appeared beyond the sensors’ region of interest boundaries. This means that these cases took place sufficiently beyond the safety distance, and had little influence on determining the behaviour of the subject vehicle.
In order to compare the similarities between the results form the proposed algorithm and human driving decisions, we introduced another type of error, acceleration error [math]\displaystyle{ a_{x, error} = a_{x, human} - a_{x, cmd} }[/math]. where [math]\displaystyle{ a_{x, human} }[/math] and [math]\displaystyle{ a_{x, cmd} }[/math] are the human driver’s acceleration history and the command from the proposed algorithm, respectively. The proposed algorithm showed more similar results to human drivers’ decisions than did the base algorithms. [math]\displaystyle{ 91.97\% }[/math] of the acceleration error lies in the region [math]\displaystyle{ \pm 1 m/s^2 }[/math]. Moreover, the base algorithm possesses a limited ability to respond to different in-lane target behaviours in traffic flow. Hence, the proposed model is efficient and safe.
Conclusion
A surrounding vehicle motion predictor based on an LSTM-RNN at multi-lane turn intersections was developed, and its application in an autonomous vehicle was evaluated. The model was trained by using the data captured on the urban road in Seoul in MPC. The evaluation results showed precise prediction accuracy and so the algorithm is safe to be applied on an autonomous vehicle. Also, the comparison with the other three base algorithms (CV/Path, V_flow/Path, and CTRV) revealed the superiority of the proposed algorithm.
Future works
1.Developing trajectory prediction algorithms using other machine learning algorithms, such as attention-aware neural networks.
2.Applying the machine learning-based approach to infer lane change intention at motorways and main roads of urban environments.
3.Extending the target road of the trajectory predictor, such as roundabouts or uncontrolled intersections, to infer yield intention.
4.Learning the behavior of surrounding vehicles in real time while automated vehicles drive with real traffic.
Critiques
The literature review is not sufficient. It should focus more on LSTM, RNN, and the study in different types of roads. Why the LSTM-RNN is used, and the background of the method is not stated clearly. There is a lack of concept so that it is difficult to distinguish between LSTM-RNN based motion predictor and motion planning.
This is an interesting topic to discuss. This is a major topic for some famous vehicle company such as Tesla, Tesla nows already have a good service called Autopilot to give self-driving and Motion Prediction. This summary can include more diagrams in architecture in the model to give readers a whole view of how the model looks like. Since it is using LSTM-RNN, include some pictures of the LSTM-RNN will be great. I think it will be interesting to discuss more applications by using this method, such as Airplane, boats.
Autonomous driving is a hot very topic, and training the model with LSTM-RNN is also a meaningful topic to discuss. By the way, it would be an interesting approach to compare the performance of different algorithms or some other traditional motion planning algorithms like KF.
There are some papers that discussed the accuracy of different models in vehicle predictions, such as Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions[1] The LSTM didn't show good performance. They increased the accuracy by combing LSTM with an unconstrained model(UM) by adding an additional LSTM layer of size 128 that is used to recursively output positions instead of simultaneously outputting positions for all horizons.
It may be better to provide the results of experiments to support the efficiency of LSTM-RNN, talk about the prediction of training and test sets, and compared it with other autonomous driving systems that exist in the world.
The topic of surround vehicle motion prediction is analogous to the topic of autonomous vehicles. An example of an application of these frameworks would be the transportation services industry. Many companies,such as Lyft and Uber, have started testing their own commercial autonomous vehicles.
Reference
[1] E. Choi, Crash Factors in Intersection-Related Crashes: An On-Scene Perspective (No. Dot HS 811 366), U.S. DOT Nat. Highway Traffic Safety Admin., Washington, DC, USA, 2010.
[2] D. J. Phillips, T. A. Wheeler, and M. J. Kochenderfer, “Generalizable intention prediction of human drivers at intersections,” in Proc. IEEE Intell. Veh. Symp. (IV), Los Angeles, CA, USA, 2017, pp. 1665–1670.
[3] B. Kim, C. M. Kang, J. Kim, S. H. Lee, C. C. Chung, and J. W. Choi, “Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network,” in Proc. IEEE 20th Int. Conf. Intell. Transp. Syst. (ITSC), Yokohama, Japan, 2017, pp. 399–404.
[4] E. Strigel, D. Meissner, F. Seeliger, B. Wilking, and K. Dietmayer, “The Ko-PER intersection laserscanner and video dataset,” in Proc. 17th Int. IEEE Conf. Intell. Transp. Syst. (ITSC), Qingdao, China, 2014, pp. 1900–1901.
[5] Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Jeff Schneider, David Bradley, Nemanja Djuric: “Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions”, 2019; arXiv:1908.00219.
[6]Schulz, Jens & Hubmann, Constantin & Morin, Nikolai & Löchner, Julian & Burschka, Darius. (2019). Learning Interaction-Aware Probabilistic Driver Behavior Models from Urban Scenarios. 10.1109/IVS.2019.8814080.