Recognizing driver braking intention with vehicle data using unsupervised learning methods

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. In this paper, a various unsupervised clustering methods will be used to build a driver braking intention predictor which can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different methods like K-means and Gaussian mixture model will be compared. Additionally, the evaluation of features from raw data, which are important to driving maneuvers clustering will be proposed. The experiment data are collected from one hybrid electric vehicle in real world. Final results show that the proposed method can detect driver’s braking intention in a very beginning moment with a high accuracy and the most important sets of feature for driving maneuver clustering will be discussed.Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. In this paper, a various unsupervised clustering methods will be used to build a driver braking intention predictor which can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different methods like K-means and Gaussian mixture model will be compared. Additionally, the evaluation of features from raw data, which are important to driving maneuvers clustering will be proposed. The experiment data are collected from one hybrid electric vehicle in real world. Final results show that the proposed method can detect driver’s braking intention in a very beginning moment with a high accuracy and the most important sets of feature for driving maneuver clustering will be discussed

Stochastic hybrid models for predicting the behavior of drivers facing the yellow-light-dilemma

We address the problem of predicting whether a driver facing the yellow-light-dilemma will cross the intersection with the red light. Based on driving simulator data, we propose a stochastic hybrid system model for driver behavior. Using this model combined with Gaussian process estimation and Monte Carlo simulations, we obtain an upper bound for the probability of crossing with the red light. This upper bound has a prescribed confidence level and can be calculated quickly on-line in a recursive fashion as more data become available. Calculating also a lower bound we can show that the upper bound is on average less than 3% higher than the true probability. Moreover, tests on driving simulator data show that 99% of the actual red light violations, are predicted to cross on red with probability greater than 0.95 while less than 5% of the compliant trajectories are predicted to have an equally high probability of crossing. Determining the probability of crossing with the red light will be important for the development of warning systems that prevent red light violations

VTrackIt: A Synthetic Self-Driving Dataset with Infrastructure and Pooled Vehicle Information

Artificial intelligence solutions for Autonomous Vehicles (AVs) have been developed using publicly available datasets such as Argoverse, ApolloScape, Level5, and NuScenes. One major limitation of these datasets is the absence of infrastructure and/or pooled vehicle information like lane line type, vehicle speed, traffic signs, and intersections. Such information is necessary and not complementary to eliminating high-risk edge cases. The rapid advancements in Vehicle-to-Infrastructure and Vehicle-to-Vehicle technologies show promise that infrastructure and pooled vehicle information will soon be accessible in near real-time. Taking a leap in the future, we introduce the first comprehensive synthetic dataset with intelligent infrastructure and pooled vehicle information for advancing the next generation of AVs, named VTrackIt. We also introduce the first deep learning model (InfraGAN) for trajectory predictions that considers such information. Our experiments with InfraGAN show that the comprehensive information offered by VTrackIt reduces the number of high-risk edge cases. The VTrackIt dataset is available upon request under the Creative Commons CC BY-NC-SA 4.0 license at http://vtrackit.irda.club

Modeling and Prediction of Driving Behaviors Using a Nonparametric Bayesian Method with AR Models

To develop a new generation advanced driver assistance system that avoids a dangerous condition in advance, we need to predict driving behaviors. Since a nonparametric Bayesian method with a two-level structure successfully predicted the symbolized behaviors only, we applied a nonparametric Bayesian method with linear dynamical systems to predicting the driving behavior. The method called the beta process autoregressive hidden Markov model (BP-AR-HMM) segments driving behaviors into states each of which corresponds to an AR model and it predicts future behaviors using the estimated future state sequence and the dynamical systems therein. Here, the segmentation as well as the parameters of the dynamical systems are determined using given training data in an unsupervised way. We carried out experiments with real driving data and found that the BP-AR-HMM predicted driving behaviors better than other methods

Modelling Stop Intersection Approaches using Gaussian Processes

International audienceEach driver reacts differently to the same traffic conditions, however, most Advanced Driving Assistant Systems (ADAS) assume that all drivers are the same. This paper proposes a method to learn and to model the velocity profile that the driver follows as the vehicle decelerates towards a stop intersection. Gaussian Processes (GP), a machine learning method for non-linear regressions are used to model the velocity profiles. It is shown that GP are well adapted for such an application, using data recorded in real traffic conditions. It consists of the generation of a normally distributed speed, given a position on the road. By comparison with generic velocity profiles, benefits of using individual driver patterns for ADAS issues are presented

Safety Control of a Class of Stochastic Order Preserving Systems with Application to Collision Avoidance near Stop Signs

Abstract-In this paper, we consider the problem of keeping the state of a system outside of an undesired set of states with probability at least P. We focus on a class of order preserving systems with a constant input disturbance that is extracted from a known probability distribution. Leveraging the structure of the system, we construct an explicit supervisor that guarantees the system state to be kept outside the undesired set with at least probability P. We apply this supervisor to a collision avoidance problem, where a semi-autonomous vehicle is engaged in preventing a rear-end collision with a preceding human-driven vehicle, while stopping at a stop sign. We apply the designed supervisor in simulations in which the preceding vehicle trajectories are taken from a test data set. Using this data, we demonstrate experimentally that the probability of preventing a rear-end collision while stopping at the stop sign is at least P, as expected from theory. The simulation results further show that this probability is very close to P, indicating that the supervisor is not conservative

Vehicle Driving Risk Prediction Based on Markov Chain Model

A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The “100-car” natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state