Query: Driver Intent Prediction

title: Long short term memory for driver intent prediction
abstract: Advanced Driver Assistance Systems have been shown to greatly improve road safety. However, existing systems are typically reactive with an inability to understand complex traffic scenarios. We present a method to predict driver intention as the vehicle enters an intersection using a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN) The model is learnt using the position, heading and velocity fused from GPS, IMU and odometry data collected by the ego vehicle. In this paper we focus on determining the earliest possible moment in which we can classify the driver's intention at an intersection. We consider the outcome of this work an essential component for all levels of road vehicle automation.

title: ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
abstract: We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space. Using the CARLA simulator, we develop a parking lot environment and collect a dataset of human parking maneuvers. We then study the impact of model complexity and feature information by comparing a multi modal Long Short Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN LSTM) to a physics based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top 1 accuracy and nearly 100% top 3 accuracy with the LSTM and CNN LSTM model) 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment improves long term predictions.

title: Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks
abstract: Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicenter of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output and ranks them according to probability. To verify the method's performance and generalizability, we present a real world dataset that consists of over 23 000 vehicles traversing five different intersections, collected using a vehicle mounted lidar based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.

title: Lane change intent prediction for driver assistance: On road design and evaluation
abstract: Automobiles are quickly becoming more complex as new sensors and support systems are being added to improve safety and comfort. The next generation of intelligent driver assistance systems will need to utilize this wide array of sensors to fully understand the driving context and situation. Effective interaction requires these systems to examine the intentions, desires, and needs of the driver for preemptive actions which can help prepare for or avoid dangerous situations. This manuscript develops a real time on road prediction system able to detect a driver's intention to change lanes seconds before it occurs. In depth analysis highlights the challenges when moving intent prediction from the laboratory to the road and provides detailed characterization of on road performance.

title: Driver intent data analysis classification
abstract: Driver intent prediction is required in order to prevent or alert other drivers maneuvers to avoid accidents. However, real time information makes this task difficult to perform an early detection/classification of any driving changing behavior. In this work, a GPS and IMU data logging from a T intersection has been analyzed and treated with the aim of obtaining the most discriminant qualitative behavior from the data available.

title: Predicting vehicle motion and driver intent using deep learning
abstract: This thesis investigates the application of Deep Learning and Mixture Models on the prediction of human drivers in traffic. The chosen approach is a Mixture Density Network where the Neural Network is composed of Long Short Term Memory units and the Mixture Model consists of univariate Gaussian distributions. The model outputs accelerations for the next four seconds in longitudinal and lateral directions as well as indicating whether the driver intends to change lane during this time or not. The results of the motion prediction is promising with a Mean Absolute Error of around 1 meter after a 4 second prediction. The intention prediction results in around 75% accuracy in identifying lane change trajectories 1.7 seconds prior to the lane change and around 95% accuracy 1 second prior to the lane change. The conclusion of the study is that in order to accurately predict humans, the model has to make use of as many variables as possible. However it will not be possible to fully predict humans by just observing the environment since there are many unobservable variables affecting the decision making. One way to take this into account is to make use of signals humans use to display their intentions, such as indicator lights.

title: Prediction of Driver Deceleration Intent in Vehicle Platoon System
abstract: This paper proposed the unscented Kalman filter (UKF) in which the state equations are defined by trend, car following and artificial neural network models to estimate the headway and velocity of platooning vehicles. Based on the estimates, the authors then attempt to predict the driver deceleration intent using a conventional car following model. The UKF integrated with the car following model is the best among the other models, but it still needs some improvement to maximize the prediction accuracy.

title: Detection and prediction of lane changes: A study to infer driver intent using support vector machine
abstract: The master thesis in this report has been made in cooperation between Scania CV AB inSodertalje and the department of machine design at KTH in Stockholm. Scania CV AB is aleading manufacturer of heavy trucks, buses and coaches, and industrial and marine engines.Increasing road safety demands for vehicles in development has resulted in a new generation ofsafety systems, called Advanced Driver Assistance Systems (ADAS) These new systems do notonly try to mitigate the effects of a crash, they also try to prevent them from happening. To avoidthat ADAS will interfere during controlled maneuvers by a driver, it is necessary to determinethe intent and current actions of the driver from sensor measurements. This importantinformation will help to improve the decision making for safety systems of when to engage inassistive actions.In this study, the possibilities to detect and predict lane changes from patterns in sensormeasurements have been made using a truck. The main objectives were to decide how toapproach this type of problem without using turn signals, estimate the accuracies that can beachieved and determine which sensors that are required to solve the task.From the concept evaluation it was determined to use the pattern recognition technique supportvector machine (SVM) for the task. To train and test the algorithm real vehicle data was used,recorded from a truck during motorway driving. From this data the algorithm was able tocorrectly classify, with accuracy of 82% between lane keep and lane change actions. Thealgorithm could also quickly detect a lane change before the first wheel crossed the lanemarking.With the set up of sensors available in the test vehicle, no significant patterns were found indriver actions prior to a lane change (the intent) Therefore, an alternative approach wassuggested that aimed to determine what the driver should prefer to do in a specific situation fromcontextual data. This additional information helped to reduce the number of false positives with8 percentage points when classifying between lane change and lane keep actions.

title: Route and Stopping Intent Prediction at Intersections From Car Fleet Data
abstract: In this paper, an approach is presented to predict the route and stopping intent of human driven vehicles at urban intersections using a selection of distinctive features observed on the vehicle state (position, heading, acceleration, velocity) For potential future advanced driver assistance systems, this can facilitate the situation analysis and risk assessment at road intersections, helping to improve the protection of vulnerable road users. After extracting recorded driving data for nine intersections (featuring over 50 000 crossings) from a database, they are assigned to possible routes and transformed from a time based representation to a distance based one. Using random decision forests, the route intent can be predicted with a mean unweighted average recall (UAR) of 0.76 at 30 m before the relevant intersection center, the stopping intent prediction scores a mean UAR of 0.78.

title: A Meta Tracking Approach for Predicting the Driver or Passenger Intent
abstract: This paper introduces a Bayesian framework for estimating the probability of a driver or passenger(s) returning to the vehicle, from the available partial (noisy) track of his/her location. The latter can be provided by a smartphone navigational service and/or other dedicated user to vehicle positioning solution, for instance RF based. The proposed approach treats the addressed intent prediction problem, i.e. not tracking the object's state (e.g. the driver/passenger position, velocity, etc. or predicting its next few values, within an object tracking formulation, leading to a Kalman filter based implementation of the inference routine. Hence, it is dubbed meta tracker in lieu of a conventional "sensor level" tracking algorithm and relies on utilising bridging distributions to encapsulate the long term dependencies in the trajectory followed by the driver or passenger as dictated by the intended endpoint, if any. Two example trajectories are shown to demonstrate the effectiveness of this flexible framework.