Pedestrian Collision Avoidance System Research Articles

Optimized Right-Turn Pedestrian Collision Avoidance System Using Intersection LiDAR

The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles.

Camera-in-the-loop based test scenario generation method for pedestrian collision avoidance system

The Pedestrian Collision Avoidance System (PCAS) of Intelligent Vehicle (IV) can be effective in preventing the occurrence of traffic accidents. However, the complicated operation environments introduce great challenges to the camera used by the PCAS. Therefore, the camera based PCAS should be fully tested and evaluated before deployment. The traditional simulation test for the camera based PCAS attempted to use geometric or physical simulation models, which have low reality and are suitable for the primary stage of the PCAS development. Camera-in-the-Loop (CIL) test is one of Hardware-in-the-Loop methods that embeds the real camera hardware into the virtual simulation system to test the camera. CIL can utilize the real hardware response while overcoming the common simulation weakness of fidelity. In this paper, we construct a CIL test platform, and propose the CIL based test scenarios generation and scenario parameter impact evaluation method for PCAS. First, we construct the CIL test platform whose image quality and functional confidence are both validated to prove CIL credibility. Second, the PCAS under the test is analyzed and the corresponding test scenario parameters are designed. In order to accelerate the test scenario generation, a Greedy Based Combination test method (GBC) based on the CIL is proposed. The Chi-square analysis and two-factor of variance analysis verification methods are used to analyze the influence of individual and multiple scenario parameters on the PCAS performance. The experiment results show that the GBC improves the test speed by 12 times compared to the traversal test, and the frequency ratio of each scenario parameter is no more than 3% different from that of the traversal test. Also, GBC has an equivalent ability to find the PCAS collision scenarios parameter combination to the traversal test.

Pedestrian Collision Avoidance in Autonomous Vehicles: A Review

Pedestrian collision avoidance is a crucial task in the development and democratization of autonomous vehicles. The aim of this review is to provide an accessible overview of the pedestrian collision avoidance systems in autonomous vehicles that have been proposed by the scientific community over the last ten years. For this purpose, we propose a classification of studies in the literature in terms of the following: (i) pedestrian detection methods, (ii) collision avoidance approaches, (iii) actions, (iv) computing methods, and (v) test methods.

Pedestrian Collision Risk Assessment Based on State Estimation and Motion Prediction

Active pedestrian collision avoidance (APCA) systems can significantly reduce road injuries and thus have attracted considerable attention from both the automobile and the transportation industries. Numerous studies have focused on APCA; however, it remains challenging to model variable and complex scenarios in a safe, efficient and low-cost way. For this purpose, this paper proposes a novel multipedestrian collision risk assessment framework comprising a motion prediction module, a collision checking module and a collision risk assessment module. First, the motion of the ego vehicle (EV) is predicted through a constant-acceleration (V-CA) model and a constant-turn-rate and constant-velocity (V-CTRV) model in different scenarios. Next, the pedestrian motion is estimated using Kalman filter (KF) approaches based on a constant-velocity (PCV) model and a constant-acceleration (P-CA) model. Then, the potential collision area (PCA) is defined according to the predicted motions of the EV and pedestrian, and the time-to collision (TTC) is selected to conduct collision checking. Finally, the most dangerous pedestrian is identified as the target with the minimum TTC in a multipedestrian scenario, and field tests are conducted on an autonomous vehicle platform. Comparative simulations indicate that the P-CA-based KF is more accurate and robust in variable-velocity scenarios than the P-CV-based KF. Furthermore, multipedestrian scenario simulations validate the effectiveness of the proposed framework.

Prediction of stopping distance for autonomous emergency braking using stereo camera pedestrian detection

Autonomous vehicle (AV) attracts a lot of research interest because of their ability to reduce road fatalities and save people's lives. Pedestrian detection and collision avoidance are the crucial parts of AVs. The existing pedestrian detection systems don't guarantee better accuracy under complex scenarios, poor light conditions, and low complexity overhead. Moreover, these systems rely on monocular camera object detection. Hence, this paper aims to propose a stereo-vision based pedestrian detection and collision avoidance system for AVs. It uses two cameras fixed at a specific distance apart to scan the environment. Once a pedestrian is detected, the system calculates the distance. The Automatic Emergency Braking System (AEBS) controller algorithm will activate the AEB if the estimated distance is less than 3.3 m, considered safe. MATLAB is used for the implementation, and the experimental results reveal that the proposed method is promising in terms of prediction accuracy and minimizing fatalities.

Collision Avoidance of Low Speed Autonomous Shuttles with Pedestrians

This paper is on a pedestrian collision avoidance system for low speed autonomous shuttles based on Vehicle-to-Pedestrian (V2P) communication. When pedestrians cannot be detected using line-of-sight sensors like camera, radar and LIDAR, V2P communication with the Dedicated Short Range Communication (DSRC) enabled pedestrian smartphone is used to detect and localize them through the in-vehicle DSRC radio used for Vehicle-to-Everything (V2X) communication. The vehicle, then, either stops or, if possible, goes around the pedestrian in a socially acceptable manner using the elastic band method for locally modifying the vehicle trajectory in real time. The elastic band method of collision avoidance is modified for fast real time execution in this paper. Along with model-in-the-loop simulations, a hardware-in-the-loop simulator using an automated driving vehicle model in the high fidelity vehicle dynamics simulation program Carsim Real Time with Sensors and Traffic with two DSRC modems emulating the vehicle and pedestrian communications is introduced and used in this paper as a prerequisite to real world experiments. Both stationary and moving pedestrians are considered in the model and hardware-in-the-loop simulations. Two real world experiments are also presented to demonstrate the V2P based avoidance of crashes between low speed autonomous shuttles and pedestrians.

Pedestrian Collision Avoidance System for Autonomous Vehicles

Pedestrian Collision Avoidance System for Autonomous Vehicles

Simulation of Automatic Pedestrian Collision Avoidance System in Autonomous Driving of Personal Mobility Vehicle

Simulation of Automatic Pedestrian Collision Avoidance System in Autonomous Driving of Personal Mobility Vehicle

WiSafe: Wi-Fi Pedestrian Collision Avoidance System

In this paper, we present a novel Wi-Fi-based pedestrian collision avoidance (PCA) system: WiSafe. Compared to vision-based and radar-based PCA systems, WiSafe has a key advantage of Non-Line-of-Sight (NLOS). WiSafe can assist drivers in discovering pedestrians in NLOS and blind spots, where the views of drivers are blocked by buildings, vehicles, or other obstacles. In particular, it is very useful when driving in poor visibility conditions such as at night, in heavy rain, or thick fog. WiSafe can save split seconds, which can potentially save lives. Instead of using wireless access in vehicular environments/dedicated short-range communications to protect pedestrians, WiSafe provides a simple, effective, and low-cost solution. Without changing Wi-Fi standards, WiSafe is compatible with 802.11a/b/g/n and can be implemented by apps on smartphones or wearable devices. The complete analysis, implementation, and experiments are presented in this paper. The results show that WiSafe can achieve and even exceed the PCA requirements.

Concept of an enhanced V2X pedestrian collision avoidance system with a cost function–based pedestrian model

ABSTRACTObjective: State-of-the-art collision avoidance and collision mitigation systems predict the behavior of pedestrians based on trivial models that assume a constant acceleration or velocity. New sources of sensor information—for example, smart devices such as smartphones, tablets, smartwatches, etc.—can support enhanced pedestrian behavior models. The objective of this article is the development and implementation of a V2Xpedestrian collision avoidance system that uses new information sources.Methods: A literature review of existing state-of-the-art pedestrian collision avoidance systems, pedestrian behavior models in advanced driver assistance systems (ADAS), and traffic simulations is conducted together with an analysis of existing studies on typical pedestrian patterns in traffic. Based on this analysis, possible parameters for predicting pedestrian behavior were investigated. The results led to new requirements from which a concept was developed and implemented.Results: The analysis of typical pedestrian behavior patterns in traffic situations showed the complexity of predicting pedestrian behavior. Requirements for an improved behavior prediction were derived. A concept for a V2X collision avoidance system, based on a cost function that predicts pedestrian near future presence, and its implementation is presented. The concept presented considers several challenges such as information privacy, inaccuracies of the localization, and inaccuracies of the prediction.Conclusion: A concept for an enhanced V2X pedestrian collision avoidance system was developed and introduced. The concept uses new information sources such as smart devices to improve the prediction of the pedestrian's presence in the near future and considers challenges that come along with the usage of these information sources.

Probabilistic risk assessment for pedestrian–vehicle collision considering uncertainties of pedestrian mobility

ABSTRACTObjectives: The uncertainties of pedestrian mobility are important factors affecting the accuracy and robustness of an active pedestrian protection system. This study is to provide the means for probabilistic risk evaluation of pedestrian–vehicle collision by counting the uncertainties in pedestrian motion.Method: The pedestrian is modeled by a first-order Markov model to characterize the stochastic properties in mobility according to field experiments of pedestrians crossing an uncontrolled road. Based on the assumption of Gaussian distribution, unscented transformation (UT) is employed to predict the collision risk probability with the symmetric σ-set constructed on the basis of discrete trajectory simulation. Simulation experiments were carried out with 10,000 Monte Carlo (MC) simulations as the reference.Results: The probability density distributions of time-to-collision, minimal distance, and collision probability estimated by UT coincide with the reference ones under various vehicle–pedestrian conflict scenarios, and the maximal deviation of collision probability from the reference is 5.33%. The UT method is about 600 times faster than the MC method (10,000 runs), which means that the proposed method has the potential for online application.Conclusions: This article presents an effective and efficient algorithm to estimate the collision probability by using a UT method to solve the nonlinear transformation of uncertainties in pedestrian motion. Simulation results show that the UT-based method achieves accurate collision probability estimation and higher computation efficiency than MC and provides more valuable information concerning collision avoidance than the deterministic methods in the design of a pedestrian collision avoidance system.

Investigation of Braking Timing of Drivers for Design of Pedestrian Collision Avoidance System

The braking behaviors of drivers when a pedestrian steps out from a sidewalk and into the street were analyzed using a driving simulator. Based on drivers' braking behavior, a braking control timing for a system for avoiding collisions with pedestrians was proposed. In this study, the subject drivers started braking at almost the same time in terms of time to collision (TTC), regardless of the velocity of the subject vehicle and the crossing velocity of the pedestrian. The results of our experiments showed that, to minimize the degree of interference between the driver and the system, the optimum timing at which to apply the braking was at a TTC of 1.3 s. Next, the drivers' braking behavior was investigated when the system controlled the braking to avoid a collision with this timing. The drivers did not exhibit any change in their braking behavior, while there was no excessive interference between the braking control system and the drivers when attempting to avoid a collision with a pedestrian, indicating that the drivers were not becoming excessively dependent on the system.

Study on Autonomous Intelligent Drive System Based on Potential Field with Hazard Anticipation

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/01.jpg"" width=""300"" />Risk potential estimation</div> Pedestrians darting out from blind spots in driver vision are typical scenarios in urban street environments, and conventional autonomous emergency braking systems reach safety limits if sensors do not detect the pedestrian in time to prevent accident or injury. The system must be able to anticipate such potential hazards and to anticipate such pedestrian action. This paper focuses on a pedestrian collision avoidance system that has a “driving-intelligence"" model. The model was designed by applying potential field theory using hazard-anticipatory knowledge. The effectiveness of the proposed system is confirmed by computer simulation. </span>

Development of Pedestrian Collision Avoidance System Based on Crosswalk Recognition by an On-Board Camera

Development of Pedestrian Collision Avoidance System Based on Crosswalk Recognition by an On-Board Camera

1014 LIDARを用いた対歩行者衝突リスク予測手法に関する研究(OS17-1 交通システムのダイナミクスと制御I,OS17 交通システムのダイナミクスと制御)

Recently, pedestrian collision avoidance systems have been launched in automobile markets and they might not be able to avoid collisions in some specific circumstances, such as when a pedestrian appears at very close distance and high velocity. To prevent collisions with pedestrian, it is necessary to develop an active safety system which considers not only overt collision risk but also potential collision risk. Therefore, this study aims to develop Hazard-anticipatory driver assistance system to prevent collisions with pedestrian considering potential collision risk. This paper describes the design of a potential hazard area detection method in intersection with poor visibility by using LIDAR as a part of the systems.

1P1-D03 危険予測運転支援のための歩行者衝突リスク予測手法に関する研究(【交通・物流部門】カー・ロボティクス)

Recently, pedestrian collision avoidance systems have been launched in automobile markets and they might not be able to avoid collisions in some specific circumstances, such as when a pedestrian appears at very close distance and high velocity. To prevent collisions with pedestrian, it is necessary to develop an active safety system which considers not only apparent collision risk but also potential collision risk. Therefore, this study aims to develop a hazard-anticipatory driver assistance system to prevent collisions with pedestrians considering potential collision risk. This paper describes the design of a potential hazard area detection method in intersection and straight road with poor visibility by using LIDAR as a part of the system.

3221 単眼カメラとミリ波レーダによる歩行者衝突回避システムの開発(OS5-3 安全・安心・防災の技術,OS5 安全・安心・防災・環境負荷低減,オーガナイズド・セッション)

3221 単眼カメラとミリ波レーダによる歩行者衝突回避システムの開発(OS5-3 安全・安心・防災の技術,OS5 安全・安心・防災・環境負荷低減,オーガナイズド・セッション)