Automated Driving Vehicles Research Articles

P‐19.2: A Review of Path Planning Algorithms for Automated Driving Vehicles

P‐19.2: A Review of Path Planning Algorithms for Automated Driving Vehicles

Koopman Operator-based Model Identification and Control for Automated Driving Vehicle

Koopman Operator-based Model Identification and Control for Automated Driving Vehicle

Study on Control for Prevention of Collision Caused by Failure of Localization for Map-Based Automated Driving Vehicle

In demonstration experiments of automated driving vehicles, lane departures and collisions with roadside structures due to poor vehicle positioning and self-localization have been reported. In this study, we propose a promising method to prevent such departures and collisions, and then validate the proposed method by applying it to an actual automated driving vehicle. The proposed method monitors the target steering angles computed by the automated driving control and limits them before commanded the actuator when there is a risk of colliding with obstacles. As the above-mentioned control is lower-level, it can prevent an automated driving vehicle from colliding with obstacles without complicating upper-level controls. Experiments on an actual automated driving vehicle showed that the steering control structure of the proposed method could prevent an automated driving vehicle from colliding with obstacles by limiting its target steering angle. In addition, the method does not impose excessive limits on the steering angle when the automated driving vehicle follows a normal path and no risk of collision exists.

An Improved Model Predictive Control-Based Trajectory Planning Method for Automated Driving Vehicles Under Uncertainty Environments

For automated driving vehicles, trajectory planning is responsible for obtaining feasible trajectories with velocity profiles according to driving environments. From the perspective of trajectory planning, multiple uncertainties of environments and tracking deviations are two significant factors affecting driving safety. The former disturbs the judgment of trajectory planning on the environments, and the latter reduces the tracking accuracy of planned trajectories. To solve these problems, an improved model predictive control (MPC) trajectory planning method is proposed in this paper. Firstly, a Kalman filter fusion method is carried out to predict obstacle trajectory and their uncertainty, which combines model-based and data-based prediction methods. Based on the prediction results, a tube-based MPC trajectory planning method is applied to plan a reference trajectory with a small tracking deviation. The tube-based MPC is composed of two parts. One is the MPC with tightened constraints that is used to plan a feasible trajectory according to a nominal vehicle system and driving environment. The other is a state feedback control that is proposed to adjust the above planned trajectory to reduce the tracking deviations. To our knowledge, this paper proposes Kalman filter fusion and tube-based MPC planning method for the first time to consider the uncertainties of trajectory prediction and tracking control meanwhile in the planning. The planning method is verified by simulations and experiments in multiple scenes. Results show that the method is suitable for both static and dynamic scenes. Compared with applying the basic prediction method, the lateral deviation of the proposed method from the ideal trajectory is decreased by 46.5%. Compared with the nominal MPC method, the lateral tracking deviations of the proposed method are decreased by 77.42%.

Self-localization with Cylindrical markers for Automated Driving Vehicles

Self-localization with Cylindrical markers for Automated Driving Vehicles

Map and Its Impact on the Functional Safety of Automated Driving Vehicles

Map and Its Impact on the Functional Safety of Automated Driving Vehicles

Physics of microscopic vehicular traffic prediction for automated driving.

With the use of microscopic traffic simulations, physical features of microscopic traffic prediction for automated driving that should improve traffic harmonization and safety have been found: During a short-time prediction horizon (about 10s), online prediction of the locations and speeds of all vehicles in some limited area around the automated-driving vehicle is possible; this enables the automated vehicle control in complex traffic situations in which the automated-driving vehicle is not able to make a decision based on current traffic information without the use of the microscopic traffic prediction. Through a more detailed analysis of an unsignalized city intersection, when the automated vehicle wants to turn right from a secondary road onto the priority road, the statistical physics of the effect of a data uncertainty caused by errors in data measurements on the prediction reliability has been studied: (i) probability of the prediction reliability has been found; (ii) there is a critical uncertainty, i.e., a maximum amplitude of errors in data measurements: when the uncertainty does not exceed the critical uncertainty, the prediction reliability probability is equal to 1, otherwise, the prediction is not applicable for a reliable automated vehicle control; (iii) physical characteristics of the microscopic traffic prediction, at which the critical uncertainty can be increased considerably, have been found; and (iv) there is an optimal automated vehicle control at which the critical uncertainty reaches a maximum value.

自動運転の基礎知識

自動運転の基礎知識

自動運転技術・取組の最近の動向

自動運転技術・取組の最近の動向

A Study on the Method for Managing Hazard Factors to Support Operation of Automated Driving Vehicles on Road Infrastructure

A Study on the Method for Managing Hazard Factors to Support Operation of Automated Driving Vehicles on Road Infrastructure

Simulation-Based Development for Active Suspension Control for Automated Driving Vehicles—Evaluation of Transferability to Real-World Testing

Simulation-Based Development for Active Suspension Control for Automated Driving Vehicles—Evaluation of Transferability to Real-World Testing

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality.

The ability of artificial intelligence to drive toward an intended destination is a key component of an autonomous vehicle. Different paradigms are now being employed to address artificial intelligence advancement. On the one hand, modular pipelines break down the driving model into submodels, such as perception, maneuver planning and control. On the other hand, we used the end-to-end driving method to assign raw sensor data directly to vehicle control signals. The latter is less well-studied but is becoming more popular since it is easier to use. This article focuses on end-to-end autonomous driving, using RGB pictures as the primary sensor input data. The autonomous vehicle is equipped with a camera and active sensors, such as LiDAR and Radar, for safe navigation. Active sensors (e.g., LiDAR) provide more accurate depth information than passive sensors. As a result, this paper examines whether combining the RGB from the camera and active depth information from LiDAR has better results in end-to-end artificial driving than using only a single modality. This paper focuses on the early fusion of multi-modality and demonstrates how it outperforms a single modality using the CARLA simulator.

Efficient stereo matching on embedded GPUs with zero-means cross correlation

Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile platforms. To resolve this trade-off, we herein propose a novel acceleration approach for the well-known zero-means normalized cross correlation (ZNCC) matching cost calculation algorithm on a Jetson Tx2 embedded GPU. In our method for accelerating ZNCC, target images are scanned in a zigzag fashion to efficiently reuse one pixel’s computation for its neighboring pixels; this reduces the amount of data transmission and increases the utilization of on-chip registers, thus increasing the processing speed. As a result, our method is 2X faster than the traditional image scanning method, and 26% faster than the latest NCC method. By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1280x384 pixel images with a maximum disparity of 128. Additionally, the evaluation results on the KITTI 2015 benchmark show that our combined system is more accurate than the same algorithm combined with census by 7.26%, while maintaining almost the same processing speed. • Introduce an effective GPU-based calculation method for ZNCC. • Propose a strategy to achieve higher parallelism of GPU threads driven by GPU cores. • Design two parallel summation methods used in our Z 2 -ZNCC. • Create FastDT, an upgraded version of the GPU-based domain transformation method.

What Determines Customer Willingness to Travel in Automated Driving Vehicles? The Perspective of Cognitive Appraisal Theory of Emotions

What Determines Customer Willingness to Travel in Automated Driving Vehicles? The Perspective of Cognitive Appraisal Theory of Emotions

Investigation on the Traffic Situations during Public Road Testing in Safety Assessment of Automated Driving Vehicles

Investigation on the Traffic Situations during Public Road Testing in Safety Assessment of Automated Driving Vehicles

Concepts for Improving Mode Awareness in Automated Driving Vehicles

Concepts for Improving Mode Awareness in Automated Driving Vehicles

Toward human-vehicle collaboration: Review and perspectives on human-centered collaborative automated driving

The last decade witnessed a great development of automated driving vehicles (ADVs) and vehicle intelligence. The significant increment of machine intelligence poses a new challenge to the community, which is the collaboration between human drivers and vehicle autonomy. As vehicle autonomy is gaining more control authority, the roles that human drivers can play in the future need to be further clarified. In this study, literature review and perspectives on the human behaviors and cognition (HBC) for ADVs toward human-autonomy (H-A) collaboration are proposed. First, the H-A collaboration basics and key factors are reviewed. Then, the HBC issues in driver behavior modeling and understanding are discussed. Specifically, two key factors are reviewed, which are human trust and situation awareness (SA). Next, HBC in two H-A collaboration-enabled vehicle control methods, namely, shared control and take-over control, are analyzed. It is shown that the human-centered collaboration strategy that integrates driver states will largely increase the acceptance of the ADVs. Then, the HBC issues in the design of human-machine-interface (HMI) for future autonomous and collaboration-enabled ADVs are discussed. Last, challenges and future works for H-A collaboration on ADVs are analyzed to contribute to the development of understandable, trustable, and acceptable ADVs.

In-vehicle displays to support driver anticipation of traffic conflicts in automated vehicles

ObjectiveThis paper investigates the effectiveness of in-vehicle displays in supporting drivers’ anticipation of traffic conflicts in automated vehicles (AVs). BackgroundProviding takeover requests (TORs) along with information on automation capability (AC) has been found effective in supporting AV drivers’ reactions to traffic conflicts. However, it is unclear what type of information can support drivers in anticipating traffic conflicts, so they can intervene (pre-event action) or prepare to intervene (pre-event preparation) proactively to avert them. MethodIn a driving simulator study with 24 experienced and 24 novice drivers, we evaluated the effectiveness of two in-vehicle displays in supporting anticipatory driving in AVs with adaptive cruise control and lane keeping assistance: TORAC (TOR + AC information) and STTORAC displays (surrounding traffic (ST) information + TOR + AC information). Both displays were evaluated against a baseline display that only showed whether the automation was engaged. ResultsCompared to the baseline display, STTORAC led to more anticipatory driving behaviors (pre-event action or pre-event preparation) while TORAC led to less, along with decreased attention to environmental cues that indicated an upcoming event. STTORAC led to the highest level of driving safety, as indicated by minimum gap time for scenarios that required driver intervention, followed by TORAC, and then the baseline display. ConclusionsProviding surrounding traffic information to drivers of AVs, in addition to TORs and automation capability information, can support their anticipation of potential traffic conflicts. Without the surrounding traffic information, drivers can over-rely on displays that provide TORs and automation capability information.

Assessing the Safety of Environment Perception in Automated Driving Vehicles

Assessing the Safety of Environment Perception in Automated Driving Vehicles

Exploiting Redundancy for Reliability Analysis of Sensor Perception in Automated Driving Vehicles

For automated driving, the perception provided by lidar, radar, and camera sensors is safety-critical. Validating sensor perception reliability with standard empirical tests is impractical, owing to the large required test effort and the need for a reference truth to identify sensor errors. To address these challenges, we investigate the possibility of estimating sensor perception reliability without a reference truth. In particular, we propose a framework to learn sensor perception reliability solely by exploiting sensor redundancies. We formulate a likelihood function for redundant binary sensor data without a reference truth and propose a Gaussian copula to model dependent sensor errors. Synthetic numerical experiments show that under an adequate dependence model, correct sensor perception reliabilities can be estimated without a reference truth. Because the selection of an adequate dependence model is challenging without a reference truth, we also investigate how inadequate dependence models influence the estimation. The proposed framework is a step toward the validation of sensor perception reliability because it could enable the learning of reliabilities from a fleet of driver-controlled vehicles equipped with series sensors.