This paper presents a design method of a Model Predictive Control (MPC) with low computational cost for a practical Adaptive Cruise Control (ACC) running on an embedded microprocessor. Generally, a problem with previous ACC is slow following response in traffic jams, in which stop-and-go driving is required. To improve the control performance, it is important to design a controller considering vehicle characteristics which significantly changes depending on driving conditions. In this paper, we attempt to solve the problem by using MPC that can explicitly handle constraints imposed on, e.g., actuator or acceleration response. Furthermore, we focus on decreasing the computational load for the practical use of MPC by using low-order prediction model. From these results, we developed ACC with high responsiveness and less discomfort even for traffic jam scene.
To detect road marks as the references of localization for automated driving technologies, we developed a detection system which is robust against disturbances such as shadows, degradation, occlusions, and changes of road surface appearances due to different weather conditions. For the accurate distance estimation, our system used the “camera-down system”; furthermore, for making the most of it, our system used the combinations of multi partial templates which focused on different parts of road marks. From evaluations of actual driving data on a prefectural road in several weather conditions, we confirmed that our proposed system has the practical performances for correctly detecting road marks including disturbed ones.
To realize a digital map for automated driving on non-highway roads, where plans for preparing highprecision 3D maps in the near future have not been determined, we propose “LeanMAP”, the contents of which are composed of map data from car navigation systems. In addition, to improve the precision of the LeanMAP contents, we propose a modification system based on actual driving data. Through the results of pilot trials on data modification in left turns at an intersection and gentle curves at a non-intersection part of a public road, we confirm the feasibility of our proposed system in that it correctly modifies the contents of LeanMAP.
Avoidance of frontal collisions by autonomous emergency braking (AEB) in sudden traffic jams has the potential to cause rear-end collisions by the following cars. In this study, finite element analyses using a human body model were performed to investigate how the muscle activations of drivers rear-ended by the following car could affect head-neck injury risks. Three muscle conditions of sleeping, relaxed, and braced drivers were assumed using a developed muscle controller. The simulation results suggest that vehicle systems that let drivers brace themselves at the onset of the AEB might be effective in reducing head-neck injury risks in this type of collisions.
In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called “survival” conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD.
Our research aims to investigate the relation between the driving behavior in the actual environment and the cognitive ability for elderly drivers. To examine the planning process during driving, we analyzed the driving behavior of passing through an intersection with a stop sign and lane changing during driving. As a result of the analysis, about 70% of elderly drivers didn’t drive safely just like driving instructors. Moreover, it was found that elderly drivers of Mild Cognitive Impairment (MCI) group had lower divided attention and alternating attention than that of non-MCI group. The drivers of MCI group were more difficult to decide a task during driving than that of non-MCI group.
This study estimates, by means of an analysis of accident data from the US, the incidence and risk of car crash related traumatic brain injuries for occupants in Japanese brand cars. The study incorporated crash type, crash severity, car model year, belt use and the victim's age and sex. Concussion risk was the highest among all brain injury categories for all crash types and severities; females were at higher risks than males. When concussions were excluded, Subdural Haemorrhages, Intracranial Haemorrhages and Sub-Arachnoid Haemorrhages comprised the most frequent injury categories. Elderly occupants were at considerably higher risks than non-elderly for these bleeding injuries.
This paper describes an estimation method of ego-vehicle’s position using a 2D map. The general approach for localization is to match the white lines between the map and the real world. However, such approaches suffer from changing the environmental conditions and painting clearance. If considerable changes occur, the false-detection rate increases due to noises and miss-detected lines. In order to solve this issue, a localization method based on the holistic road area detection is proposed. First, the road area is extracted from a predefined 2D boundary map. Next, the real world road area is detected using LiDAR and converted into the binary image plane. Finally, an image registration technique is applied to calculate the overall matching score of the road area between the map and LiDAR images. The proposed method has provided an accurate estimation against environmental changes with low-cost calculation based on the simulation results. In addition, the validation of the proposed method in the real world has performed less than 0.2 m for the estimation error.
Distracted driving has become an emerging concern for road safety in the past decade. Efforts have been made to develop in-vehicle active safety systems that could detect driver distraction. However, most methods focused on detecting a distracted driver of the host vehicle (ego-vehicle). Given that a distracted driver poses increased crash risk not only to him/herself but also to other road users, it may be beneficial to investigate ways to detect a distracted driver from a surrounding vehicle. This paper proposes a method to estimate the kinematics of a lead vehicle solely based on the sensory data from a host vehicle. The estimated kinematics of the lead vehicle include its lane position, lateral speed, longitudinal speed, and longitudinal acceleration, all of which may be potentially useful to detect distracted driving. The method was developed and validated using an existing naturalistic driving study, Safety Pilot Model Deployment, which collected a large scale of driving data in real-world roadways. The method utilizes signals from a camera-based Mobileye® system and other host vehicle sensory channels such as speed and yaw rate. Sensor fusion techniques were used to improve the accuracy of the estimation. The validation results show that the method was able to capture the lead vehicle’s kinematics within a fairly small error range. The method could be potentially used to develop in-vehicle systems that are able to monitor the behaviors of its surrounding vehicles and detect distracted or impaired driving.
Audio warning system of collision warning system had been investigated extensively in previous studies; however, only a few of them focused on warning effectiveness under varied situational urgency. Their results suggested that warning signal should be tested under several situational urgencies to confirm its effectiveness. This leads to the objective of this study, which is to explore the effect of varied auditory warning urgency and varied situational urgency on collision avoidance performance. This paper performed an experimental evaluation for four audio warning conditions: high urgency, medium urgency, low urgency and none(no warning), with three situational urgencies: high, medium, and low situational urgency. The results indicate that varied situational urgency has some influence on the collision avoidance performance. In the aspect of brake reaction time, this study result suggests that warning with higher perceived urgency tends to improve brake reaction time in general. For brake profile, increasing the warning urgency up to the medium level improves braking behavior while high warning urgency tends to worsen it. In mean deceleration aspect, in low situational urgency, Mean deceleration tends to be decreased as the urgency of the warning increased up until the medium warning urgency, but high warning urgency increases the mean deceleration instead. Overall, medium warning urgency causes more stable and more appropriate braking response among all designed warning urgency.