Driver's Lane Change Intention Research Articles

A self‐learning human‐machine cooperative control method based on driver intention recognition

AbstractHuman‐machine cooperative control has become an important area of intelligent driving, where driver intention recognition and dynamic control authority allocation are key factors for improving the performance of cooperative decision‐making and control. In this paper, an online learning method is proposed for human‐machine cooperative control, which introduces a priority control parameter in the reward function to achieve optimal allocation of control authority under different driver intentions and driving safety conditions. Firstly, a two‐layer LSTM‐based sequence prediction algorithm is proposed to recognise the driver's lane change (LC) intention for human‐machine cooperative steering control. Secondly, an online reinforcement learning method is developed for optimising the steering authority to reduce driver workload and improve driving safety. The driver‐in‐the‐loop simulation results show that our method can accurately predict the driver's LC intention in cooperative driving and effectively compensate for the driver's non‐optimal driving actions. The experimental results on a real intelligent vehicle further demonstrate the online optimisation capability of the proposed RL‐based control authority allocation algorithm and its effectiveness in improving driving safety.

Driver Lane Change Intention Recognition Based on Attention Enhanced Residual-MBi-LSTM Network

Accurate identification of lane-changing intention can effectively assist intelligent driving vehicles in terms of decision-making and trajectory planning, which plays a significant role in enhancing driving safety by reducing traffic accidents caused by lane-changing. Based on the trajectory characteristics and vehicle interaction information, an attention-enhanced bidirectional multi-layer residual long-short term memory neural network (Attention Enhanced Residual-MBi-LSTM) model is proposed for lane change intention recognition in this paper. Firstly, an EWMA filter is employed to smooth the noisy data collected from the vehicle. Then a four-layer bidirectional residual LSTM memory (Residual-MBi-LSTM) structure is used to extract lane-changing features from the historical driving trajectories of ego-vehicle and vehicle interaction information. Besides, the attention mechanism is added to adjust the weight of data in different time frames. After that, the current lane-changing probability is calculated and output by the Softmax function. Finally, the vehicle lane-changing intention recognition model is firstly trained and then verified in the HighD dataset. According to the HIL experiment, the proposed model has the ability to identify driver intention on average of 2.07 seconds in advance.

Driver lane change intention recognition in the connected environment

The connected environment provides information on surrounding traffic and areas beyond the visual range to improve driving behavior and avoid dangerous incidents. However, due to the novelty of the connected environment and the scarcity of connected data, current research on driver lane change intention in this field has received little attention. In this work, we designed a typical lane change scenario in the connected environment based on a driving simulator and real-time collection of multi-modal data from eye trackers, driving simulators, and a connected platform. The driver’s eye movement, head rotation, vehicle movement, and the driver’s maneuver parameters were analyzed, revealing a significant difference between the lane change intention and lane keep stages in the connected environment. In addition, the length of the intention time window with connected information (6.5 s) was longer than that without connected information (4 s). The bi-directional long and short-term memory network based on the attention mechanism (AT-BiLSTM) was used to establish a lane change intention model. The accuracy of the lane change intention model based on the proposed AT-BiLSTM algorithm surpassed that of existing machine learning algorithms. The recognition accuracy of the lane change intention model was 93.33% at 3 s prior to the lane change. The conclusions of this study are of great significance for the development of a side warning assist system in future connected environments.

Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory

Rapid and correct estimation of driver lane change intention plays an important role in the advanced driver assistance system (ADAS), which could make the driver improve the reliability of the ADAS system and help to decrease driver workload. In this study, a method based on the long short-term memory network (LSTM) and Dempster–Shafer evidence theory is proposed. The model consists of a preliminary decision-making label and a final decision-making label. Driver visual information, head orientation, and vehicle dynamics are collected by preliminary decision-making label. Then, LSTM is used to calculate the initial probability of the driver lane change (left, right, and lane keeping) maneuver intention. The outputs of LSTM are normalized and assigned a basic probability by the Dempster–Shafer evidence theory. The final decision-making label analyzes the information and outputs the probability of each lane change intention and the decision is to identify the driver's current intention. The experimental results show that the accuracy of the model is 90.7% for the intention of changing left and 89.1% for the intention of changing right. The outcome of this work is an essential component for all levels of road vehicle automation.

Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System

Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver's lane change intention. By using a deep learning-based method to model a driver's decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver's intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.

Early Recognition of Driving Intention for Lane Change Based on Recurrent Hidden Semi-Markov Model

Timely recognition of driving intention is crucial in the design of a safe and effective driving assistance system. This study proposes an efficient recognition approach based on Nonlinear Polynomial Regression (NPR) and Recurrent Hidden Semi-Markov Model (R-HSMM) to recognize the driver lane-change intention accurately in the early stage. The NPR model is utilized to transform the input signal amplitude into the standard form in order to improve system adaptability. Besides, an unsupervised time-series segmentation method named the Toeplitz Inverse Covariance-based Clustering (TICC) is applied to label the driving data automatically. The R-HSMM is utilized as a time-series classifier to classify the driving intention during the lane-change process into predefined categories based on the signals processed by the NPR. The proposed method is verified by the experiments with a driving simulator. The experimental results show that the proposed method can recognize driver intention earlier than the popular recognition methods, and also can reduce the number of false warnings during the lane-change process, which has great significance for driving safety improvement. Moreover, the proposed method can adapt well to various vehicle speeds achieving stable recognition performance.

An ensemble deep learning approach for driver lane change intention inference

With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers’ intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.

An investigation on coordination of lane departure warning based on driver behaviour characteristics

As an important part of Advanced Driver Assistance Systems (ADAS), Lane Departure Warning System (LDWS) plays a significant role in lane departure prevention and reducing traffic accidents caused by lane departure. In order to improve the warning effect of the system as well as driver acceptance, this paper describes an LDWS algorithm for personalised driving assistance. The proposed combination algorithm consists of a multi-mode Time to Lane Crossing (TLC) and a Future Offset Distance (FOD) based on driver behaviour characteristics. To detect driver's lane change intention, the steering behaviour has been developed incorporating vehicle states and road curvature. Driving simulator tests are conducted to validate the lane departure warning algorithm with multi-mode based on TLC and FOD under various driving situations. The obtained test results are consistent with the expected performance.

An investigation on coordination of lane departure warning based on driver behaviour characteristics

As an important part of Advanced Driver Assistance Systems (ADAS), Lane Departure Warning System (LDWS) plays a significant role in lane departure prevention and reducing traffic accidents caused by lane departure. In order to improve the warning effect of the system as well as driver acceptance, this paper describes an LDWS algorithm for personalised driving assistance. The proposed combination algorithm consists of a multi-mode Time to Lane Crossing (TLC) and a Future Offset Distance (FOD) based on driver behaviour characteristics. To detect driver's lane change intention, the steering behaviour has been developed incorporating vehicle states and road curvature. Driving simulator tests are conducted to validate the lane departure warning algorithm with multi-mode based on TLC and FOD under various driving situations. The obtained test results are consistent with the expected performance.

A Two-Stage Learning Framework for Driver Lane Change Intention Inference

Abstract With the rapid development of intelligent vehicles, human drivers are sharing the control authority with the automation functionalities. The mutual understanding between the intelligent vehicle and the human driver is the key to the efficient multi-agent teaming and the collaborative driving system. In this study, a two-stage learning framework for spatial-temporal driver lane change intention inference is developed. The two-stage learning framework contains two separate parts. First, an operational level driving behavior recognition system based on the deep convolutional neural network (CNN) is developed to recognize the driver behaviors, including mirror checking and normal driving. Then, based on the driver behavior features from the CNN, the sequential feature vector will be used for the construction of driver intention inference model based recurrent neural networks (RNN) and long short-term memory (LSTM). The proposed model achieved a state-of-the-art result on the driver lane change intention inference task. Based on the naturalistic driving dataset, the model achieved over 91% accuracy for lane change and lane-keeping intention prediction. The two-stage learning framework can significantly increase the model flexibility and accuracy, which makes it easier to be implemented and updated in intelligent vehicles.

Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context, as well as the driver status since ADAS share the vehicle control authorities with the human driver. This paper provides an overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consist of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.

Adaptive fuzzy pattern classification for the online detection of driver lane change intention

In this paper we introduce a new fuzzy system using adaptive fuzzy pattern classification (AFPC) for data-based online evolvement. The fuzzy pattern concept represents an efficient tool for handling uncertainty in multi-dimensional data streams and combines powerful performance, flexibility and meaningful interpretability within one consistent framework. We outline AFPC for non-linear, multi-dimensional transition processes, namely, for the identification of lane change intention in car driving. While lane changes are rare, they are highly safety-relevant transition processes, showing high fuzziness and large individual and inter-individual variations (e.g., in lane change duration). The method employs a combined knowledge- and data-based approach, and the underlying fuzzy potential membership function concept models expert knowledge, closely mirroring human cognition. The design of AFPC comprises (I) an initial training phase (off-line and supervised), which generates a meaningful start-classifier, (II) an online application phase, and finally (III) an evolvement phase (online and unsupervised). Here we consider parametric and structural adaptations and discuss prospects and future challenges. Furthermore, we present specific modeling results for such online data from a real driving study. Next-generation advanced driver assistance systems, as well as autonomously driven vehicles need to evolve, in terms of parameters and structure, based on online real-time data. AFPC presents an efficient tool for application in this area and others (e.g., medicine).