Environment-Detection-and-Mapping Algorithm for Autonomous Driving in Rural or Off-Road Environment

Abstract—This paper presents an environment-detection-and-mapping algorithm for autonomous driving that is provided in real time and for both rural and off-road environments. Environment-detection-and-mapping algorithms have been de-signed to consist of two parts: 1) lane, pedestrian-crossing, and speed-bump detection algorithms using cameras and 2) obstacle detection algorithm using LIDARs. The lane detection algorithm returns lane positions using one camera and the vision module “VisLab Embedded Lane Detector (VELD), ” and the pedestrian-crossing and speed-bump detection algorithms return the position of pedestrian crossings and speed bumps. The obstacle detection algorithm organizes data from LIDARs and generates a local obstacle position map. The designed algorithms have been im-plemented on a passenger car using six LIDARs, three cameras, and real-time devices, including personal computers (PCs). Vehicle tests have been conducted, and test results have shown that the vehicle can reach the desired goal with the proposed algorithm. Index Terms—Autonomous driving, lane detection, obstacle de-tection, pedestrian-crossing detection, speed-bump detection. I

Bidirectional Long Shot-Term Memory-Based Interactive Motion Prediction of Cut-In Vehicles in Urban Environments

This paper presents an interactive motion predictor to infer the intention of cut-in vehicles using a bidirectional long short-term memory (Bi-LSTM) module. The proposed predictor consists of three modules: maneuver recognition, trajectory prediction, and interaction. The driving data for training and validating the Bi-LSTM module were collected by sensors mounted on an autonomous vehicle (AV). In total, 3,828 trajectories of human-driven vehicles around the AV are accumulated in a global coordinate system. After postprocessing the collected trajectories, 83,188 and 35,652 data samples were used to train and validate the Bi-LSTM module, respectively. In the Bi-LSTM module, a maneuver is defined as the desired driving lane of a vehicle, which extend the behavior coverage of the proposed approach. The trajectory prediction step is based on the path-following model with a motion parameter estimator to predict the trajectories for all possible maneuvers. The interaction module considers the likelihood of each maneuver and the collision risk to determine the future trajectories of the surrounding vehicles in terms of the driving scene. The proposed predictor was evaluated in terms of its prediction accuracy and its effects on the motion planner of the AV. It has been shown that the AV benefits from the improved motion prediction of target vehicles provided by the proposed predictor with respect to enhanced safety and reduced control effort in the case of cut-in situations.Y

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

This paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must be considered during autonomous overtaking. These are considered through safe distance based on human driving behavior. Since all vehicles cannot be equipped with a vehicle to vehicle communications at present, autonomous vehicles should perceive the surrounding environment based on local sensors. In this paper, virtual targets are devised to cope with the limitation of cognitive range. A probabilistic prediction is adopted to enhance safety, given the potential behavior of surrounding vehicles. Then, decision-making and motion planning has been designed based on the probabilistic prediction-based safe distance, which could achieve safety performance without a heavy computational burden. The algorithm has considered the decision rules that drivers use when overtaking. For this purpose, concepts of target space, demand, and possibility for lane change are devised. In this paper, three driving modes are developed for active overtaking. The desired driving mode is decided for safe and efficient overtaking. To obtain desired states and constraints, intuitive motion planning is conducted. A stochastic model predictive control has been adopted to determine vehicle control inputs. The proposed autonomous overtaking algorithm has been evaluated through simulation, which reveals the effectiveness of virtual targets. Also, the proposed algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable overtaking driving has been demonstrated using a test vehicle.Y

Target Vehicle Motion Prediction-Based Motion Planning Framework for Autonomous Driving in Uncontrolled Intersections

This paper presents a motion-planning framework for urban autonomous driving at uncontrolled intersections. The intention and future state of the target vehicles are predicted using information obtained from the environment sensors. The target state prediction module employs an Interacting Multiple Model (IMM) filter to infer the intention of targets. The prediction results of each model of the IMM filter are fused to predict the future state of targets. The proposed predictor uses the intelligent driver model based-driver behavior model to construct the local filter of IMM. The driving mode decision is realized as a state machine consisting of two phases, 'Approach' and 'Risk Management'. The risk management phase is composed of two sub-modes, 'Cross' and 'Yield'. The state transition conditions between phases and modes are defined by introducing the concepts of 'Critical gap' and 'Follow-up gap'. Based on the determined driving mode, the motion planning module consists of two sub-modules for each phase. The required deceleration determination for the approach phase is proposed to consider the occluded region in order to prevent inevitable collisions caused by fast approaches. The model predictive controller for the risk management phase is designed to determine the desired acceleration to guarantee safety and prevent unnecessary deceleration simultaneously. Both computer simulation studies and vehicle tests are conducted to evaluate the proposed framework. The results indicate that the proposed framework ensures the safety at uncontrolled intersections with a driving pattern similar to that of a driver.N

Design of Longitudinal Control for Autonomous Vehicles based on Interactive Intention Inference of Surrounding Vehicle Behavior Using Long Short-Term Memory

This paper presents a method of intention inference of surrounding vehicles' behavior and longitudinal control for autonomous vehicles. A Recurrent Neural Network (RNN) based on Long Short-Term Memory (LSTM) cells has been used to predict the future driving lane of surrounding vehicles. Interaction among the adjacent vehicles is considered in the RNN to improve the behavior prediction accuracy. A Model Predictive Control (MPC) has been designed to derive the longitudinal control input of the autonomous vehicle in a predictive manner based on the prediction results. The proposed behavior prediction algorithm has been evaluated according to its behavior classification accuracy. Also, the longitudinal control algorithm has been validated in car-following scenarios with the existence of cut-in vehicles via computer simulations. Experimental results show that the proposed predictor improves the performance of behavior prediction and the longitudinal control method enables autonomous vehicles to maintain safety with respect to the cut-in vehicles with proper ride quality.N

Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections

This paper presents a surround vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). The motion predictor is trained using the states of subject and surrounding vehicles, which are collected by sensors mounted on an autonomous vehicle. Data on 484 vehicle trajectories were collected from real traffic situations at multi-lane turn intersections. 11,662 and 4,998 samples acquired from the vehicle trajectories were used to train and evaluate the networks, respectively. A motion planner based on Model Predictive Control (MPC) is designed to determine the longitudinal acceleration command based on the predicted states of surrounding vehicles. The future states of the subject vehicle derived by MPC is used as an input feature to reflect the interaction of subject and target vehicles in LSTM-RNN based motion predictor. The proposed algorithm was evaluated in terms of its accuracy and its effects on the motion planning algorithm based on the driving data sets. The improved prediction accuracy substantially increased safety by bounding the prediction error within the safety margin. The application results of the proposed predictor demonstrate the improved recognition timing of the preceding vehicle and the similarity of longitudinal acceleration with drivers.Y

A novel approach to design and control of an active suspension using linear pump control-based hydraulic system

This paper presents a novel design and control method of an active suspension system using a linear pump control-based hydraulic system for a cost-effective application. Various active suspension systems have been proposed and applied to vehicles due to its ability to improve ride comfort and handling performance even though these active suspension systems are not popular because of their complexity, high cost, heavyweight, and low power efficiency. A new type of active suspension actuator system was designed and validated herein based on the methods of actuator sizing and modified control scheme to address the aforementioned issues. System power capability has been analyzed under various dynamics and road conditions. Active suspension actuator components have been designed based on the results. The electro-hydraulic system is powered by a battery to reduce the energy consumption of the system; hence, it is operated by torque on demand. A double-acting linear hydraulic motor pump with a dual rack and pinion has been proposed for hydraulic force control with a simple on/off switch operation. The actuator force has been controlled by continuous linear motor pump displacement control via torque control using a three-phase synchronous brushless alternative current motor. Dynamic performance evaluation of the actuator system has been conducted using AMESIM and actual rig test. Active height and roll control algorithms for the enhancement of vehicle dynamics considering actuator dynamics have also been developed and validated through the rig and real vehicle tests. The evaluation results showed that the linear motor pump-based active suspension system performs as well as the previous complicated hydraulic active suspension system. The new active system proposed in this study was able to improve the vehicle dynamics using cost-effective actuation system significantly.N