Pongsathorn Laboratory


Smart Mobility Research Center

Research Topics

Automated Driving and ADAS

Advanced Driver Assistance Systems with Potential Risk Prediction

To enjoy life independently, elderly people need safe and secured transportation. Among various transportation tools, automobiles are frequently used in terms of supporting daily life.
However, elderly drivers are more likely to have accidents due to their declined physical ability. Therefore, in order to help the elderly drivers to achieve safe and secured driving, we are conducting researches and validation experiments on sensor technology, risk prediction technology, collision avoidance technology, etc. By the above researches and developments, we aim to penetrate an autonomous driving intelligence system to into the market which can ensure the safe driving by recovering degraded performances of recognition, decision-making and operation for elderly drivers and avoiding the potential accidents.

The above researches had been included in the project “Autonomous Driving Intelligence System to Enhance Safe and Secured Traffic Society for Elderly Drivers” of Strategic Promotion of Innovation Research and Development Program (S-Innovation) sponsored by Japan Science and Technology Agency (JST) from 2010 to 2020.

Motion Planning and Control Algorithm Based on Risk Potential Field

This research aims to develop an “Autonomous Driving Intelligence System” to prevent risk of accidents and enhance driving safety while keeping human drivers in the control loop. The proposed system focuses on two key technologies: Risk-predictive driving intelligence model and Shared control between the driver and the assistance system. The first key technology is to embed an experienced driver model for recovering degraded performances of recognition, decision-making and operation of drivers. In the driver assistance system design, the experienced driver model contains knowledge-based “risk-prediction mechanism” to avoid accidents in risky driving situations which the system is activated in early stage to assist safe driving when the collision risk becomes imminent. The second key point is “Shared control” using haptic interface of steering and pedal to realize good cooperative characteristics between the driver and the system by optimizing the assistance level while minimizing the interference with human driver operation.

  • A part of the results of this research received the 2016 Masao Horiba Award

Braking Control Assistance System Based on Risk Predictive Driver Model

Recently, Autonomous Emergency Braking System (AEBS) have been already introduced to the markets to realize road traffic zero fatalities, and humans positively evaluated the performance of such assistance system safety function. However, such assistance system reaches its limit when there are unexpected moving obstacles appearing suddenly from poor visibility area, such as a corner of blind intersection, and a space behind a parked vehicle. This research focuses on a parked vehicle overtaking scenario which an occluded pedestrian suddenly might intend to cross a road, and risk predictive shared deceleration control system which can assist in avoiding potential collision risk is proposed. By conducting the driving simulator experiment, we investigated its functionality and the effectiveness of the proposed pre-braking intervention support.

  • A part of the results of this research received the 68th JSAE outstanding technical paper Award.

Situational Risk Assessment Method Based on Driving Context Learning

The near-crash events relevant to vulnerable road users can lead to serious accidents. This research proposes a potential risk estimation method for predicting the criticality of driving situations with respect to driving context and driving behavior state. Near-miss incident events were extracted to conduct human error analysis as well as cause-and?effect chain studies of accidents, and the annotation parameters that describe the driving context were investigated to find their influence on the criticality of the recorded incidents. The obtained results can be used to develop next generation ADAS or to improve algorithms for autonomous driving for increasing their safety as well as the driver acceptance.

Human-Machine Cooperative Steering Assistance by Shared Control

Shared control between a human driver and a machine is an important issue of human-centered automated driving technology. This research proposes a new shared control system which combines haptic steering control torque together with velocity control in order to conduct stable and appropriate operating guidance resulting in the same way of an expert driver. This system has two following features. The first feature is a reference driver model which calculates the reference steering wheel angle and acceleration/deceleration command simultaneously by using sequential reference path curvature generation. The second feature is the haptic shared control torque controller in which the intensity of the haptic control torque gain are adjusted by using the key concept of the shared ratio (SR). From the driving simulator experiment employed ten subject drivers, the effectiveness of the proposed system in the curved path tracking driving scenario is verified from the viewpoint of path tracking performance and the steering angular velocity.

  • A part of the results of this research received the best paper award at the 14th International Symposium on Advanced Vehicle Control (AVEC2018).

Vehicle Dynamics Control for Collision Avoidance in Intersections

Risk Predictive Driver Model in Unsignalized Intersections for Preventing Pedestrian Accidents

Risk prediction relevant to pedestrians darting out to roadways is a promising key technology to reduce the number of crashes with pedestrians in unsignalized intersections. The goal of this research is to develop an advanced driver assistance system for preventing crashes with pedestrians in unsignalized intersections by considering knowledge in risk prediction. The reference longitudinal driver model with risk predictive driving behavior is constructed based on the driving data of driving instructors recorded by the continuous logging drive recorder. The driver speed control model is embedded in the proposed driver assistance system and the effectiveness of the driver assistance system is verified.

  • A Part of the results of this research received the 64th JSAE Asahara science award at the 2014 JSAE Annual Congress (Spring).

Risk Predictive Braking with Collision Avoidance in Intersection Right Turn Maneuver

This research focuses on the intersection right-turn scenario where an object darts out from blind spot of a congested oncoming traffic at the intersection. In such scenario, due to physical friction limit, collision may not be avoided by emergency braking of a driver or an active safety function such as Autonomous Emergency Braking System (AEBS). For improving safety under the limited physical friction potential, the main objective of this research is to develop a risk predictive right-turn assistance system by predicting the potential oncoming object to reduce the vehicle velocity in advance. The blind corner can be detected by on-board sensors without requiring information from infrastructure. The intersection right-turn assistance system is developed and it is evaluated that the system effectively decelerates the ego vehicle to a safe velocity that enables to avoid conflict by AEBS in the emergency cases.

  • A part of the results of this research received the FAST-zero’ 17 (4th international symposium on future active safety technology towards zero-traffic-accidents).best paper award.

Collision Avoidance Control Based on Virtual Repulsive Force Field

Advanced driver assistance systems and autonomous driving systems are being enhanced to deal with various types of collision use-cases. To handle those complicated scenarios, the rational and understandable integrated vehicle control algorithm on a 2D planar motion is required. Here, a new 2D motion control methodology assuming virtual repulsive force generated from obstacles is introduced. The use-case functions are the lane departure prevention vehicle control and the forward obstacle avoidance. The simulation is conducted in various driving situations not only for single lane change for the forward obstacle avoidance but also for the case of arbitrary approach angle with respect to direction towards obstacles. Assuming virtual repulsive force field, the collision avoidance path can be calculated as the combination of two parabolic curves and the control activation points also can be calculated analytically.

COI Mobility Innovation Center

Our laboratory is collaborating with the Nagoya University Center of Innovation, on research related to the topic of “Empowering an aging society through advanced mobility”. As a part of intelligent car research, in order to bring the application of slow self-driving cars at an early date, relevant research is being carried out on the construction of the standard driver model.

Trajectory Planning and Motion Control of Low-Speed Autonomous Vehicle

In recent years, mobility supported by low-speed autonomous driving service has been considered for vulnerable road users. However, driving at a low-speed region might have a problem in disturbing normal traffic flow when such vehicles appear in public roads, and consequently, the social acceptance of such mobility service is not high. Therefore, it is required to detect an approach of rearward vehicles by using sensors and to move the vehicle to a free space where the ego vehicle does not disturb the traffic of normal passenger cars which are driving at a higher speed. In this work, by measuring the brake operation of the rearward car concerning the low-speed vehicle motion in the experiment, the appropriate avoidance start timing is investigated. In addition, an optimal trajectory planning of the ego vehicle in lane-change maneuver is proposed, and the motion control is designed to track the desired path. We generate a smooth evacuation trajectory that minimizes the lateral acceleration when constant deceleration is assumed, and verify its effectiveness by the following simulation using an equivalent two-wheel model or a nonlinear four-wheel vehicle model.

Standard Driver Model Research

(1)Use Dynamic Maps for Information Sharing

A dynamic map is a framework to attach various dynamic information (vehicle position, traffic jam, weather, etc.) to an ordinary map. By sharing the information of pedestrians detected by sensors mounted on other vehicles on the dynamic map, pedestrians in the blind spot of the ego vehicle can be detected timely.

(2)Right-left Turn Merging using Dynamic Map

In order to expand the adaptability of autonomous vehicles in a complex traffic environment, in this work, we proposed a control system for left-right turn merging using the dynamic map.

Chassis Dynamics and Control

Suspension Control and Road Preview Sensing

This research proposes a real-time road surface profile estimation method ahead of the vehicle using a stereo camera for preview suspension control. The Performance of suspension control system can be enhanced significantly with the information of road surface profile preview. We verified the estimation accuracy of road surface profile using the proposed image processing method and the test drive shows that the ride comfort can be improved by preview semi-active suspension control system.

  • A part of the results of this research received the Presentation Award at the 21st Intelligent Mechatronics Workshop (IMEC2016).
  • A part of the results of this research received the Award (2018 JSME Transportation and Logistics Division, Certificate of Merit for Paper and Presentation).

Automated Parking Control for Semi-trailer Vehicles

As the demand for road freight transportation increases, articulated vehicles (semi-trailer) have the advantage that the maximum load capacity is large. However, there are some problems that high driving skill is required because it is necessary to perform unique steering during backward parking. This research proposes the concept of the parking assist system and path tracking controller. The control system consists of a Pure Pursuit Motion Planning method to determine the reference path tracking and a feedback controller for stabilization of the articulation angle. The experiment using an actual semi-trailer vehicle was carried out and the effectiveness of the proposed approach was verified by evaluating the precision of the parking position.

Enhancing Handling and Stability of Load-Sensitive Electric Vehicle by In-Wheel-Motor Torque Distribution Control

From the viewpoint of energy-efficient vehicles and emission problems, lightweight electric vehicles are expected to be spread. However, lightweight electric vehicles are sensitive to loading condition and tend to make drivers control the vehicle in more difficult manner. As a lightweight electric vehicle equipped with in-wheel motors which can independently control each drive wheel torque, Direct Yaw-moment Control (DYC) is effective as a method for improving handling and stability. This research proposes DYC to compensate the changes in the vehicle handling dynamics due to variation in loading conditions. The DYC system is composed of a feedforward of the steering angular velocity and a feedback of the yaw rate. Experiments on the double-lane-change test with a driving simulator were conducted to verify the effectiveness of the proposed DYC system.


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