Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios

Modelling shared space users via rule-based social force model

The promotion of space sharing in order to raise the quality of community living and safety of street surroundings is increasingly accepted feature of modern urban design. In this context, the development of a shared space simulation tool is essential in helping determine whether particular shared space schemes are suitable alternatives to traditional street layouts. A simulation tool that enables urban designers to visualise pedestrians and cars trajectories, extract flow and density relation in a new shared space design and achieve solutions for optimal design features before implementation. This paper presents a three-layered microscopic mathematical model which is capable of representing the behaviour of pedestrians and vehicles in shared space layouts and it is implemented in a traffic simulation tool. The top layer calculates route maps based on static obstacles in the environment. It plans the shortest path towards agents' respective destinations by generating one or more intermediate targets. In the second layer, the Social Force Model (SFM) is modified and extended for mixed traffic to produce feasible trajectories. Since vehicle movements are not as flexible as pedestrian movements, velocity angle constraints are included for vehicles. The conflicts described in the third layer are resolved by rule-based constraints for shared space users. An optimisation algorithm is applied to determine the interaction parameters of the force-based model for shared space users using empirical data. This new three-layer microscopic model can be used to simulate shared space environments and assess, for example, new street designs

Socially Aware Motion Planning with Deep Reinforcement Learning

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Mobile Safety Application for Pedestrians

Vulnerable Road User (VRU) safety has been an important issue throughout the years as corresponding fatality numbers in traffic have been increasing each year. With the developments in connected vehicle technology, there are new and easier ways of implementing Vehicle to Everything (V2X) communication which can be utilized to provide safety and early warning benefits for VRUs. Mobile phones are one important point of interest with their sensors being increased in quantity and quality and improved in terms of accuracy. Bluetooth and extended Bluetooth technology in mobile phones has enhanced support to carry larger chunks of information to longer distances. The work we discuss in this paper is related to a mobile application that utilizes the mobile phone sensors and Bluetooth communication to implement Personal Safety Message (PSM) broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V) based safety warning structure. This implementation allows the drivers to receive a warning on their mobile phones and be more careful about the pedestrian intending to cross the street. As a result, the driver has much more time to safely slow down and stop at the intersection. Most importantly, thanks to the wireless nature of Bluetooth connection and long-range mode in Bluetooth 5.0, most dangerous cases such as reduced visibility or No-Line-of-Sight (NLOS) conditions can be remedied

Accuracy Improvement of Pedestrian Trajectory Prediction by an Extended Kalman Filter and Pedestrian Behavior Classification

The objective of this thesis is to improve the accuracy of predicting motion trajectory, i.e., speed and direction, of a pedestrian in front of an Ego Vehicle which has a Mobileye camera with an advanced driver assistance system (ADAS). The Ego Vehicle captures and records videos of pedestrians in front of it, and these videos are analyzed to predict a pedestrian trajectory from instantaneous, random actions of a pedestrian. Instant actions include, but are not limited to, walking at a constant speed, sudden accelerations/decelerations, sudden dodging from the Ego Vehicle, sudden advancements to the Ego Vehicle, sudden withdrawals or sudden stops at the road edge, etc. Pedestrian positions and motion data from the videos can be used to estimate pedestrian state parameters and predict pedestrian movement. The pedestrian videos contain noises due to the nonlinear trajectory of a pedestrian and the Ego Vehicle. An extended Kalman filter (EKF) and pedestrian behavior classification are applied to these pedestrian videos to obtain a more accurate pedestrian trajectory. The EKF is used to suppress noises from the videos and aids in predicting the next state of pedestrian movement. The EKF can reduce noises in a nonlinear system. The EKF is an efficient and effective tool in creating more stable and smoother pedestrian positions from the Ego Vehicle videos, as we have demonstrated from analyzing pedestrian trajectories from real-world videos. These new position data inputs are used to calculate the new velocity of a pedestrian. This new velocity is averaged over 30 consecutive video frames to obtain a more accurate and stable velocity. After the new position and velocity are calculated, pedestrian behavior classification is applied to the data to calculate and group pedestrian behaviors into instant actions. The behavior classification is based on the estimation of the heading angle and acceleration of a pedestrian. The combination of the extended Kalman filter and behavior classification forms a more accurate pedestrian trajectory prediction system. This approach is verified with 12 hours of ADAS camera Mobileye videos from an experimental car test site within a simulated urban area. Ten cases of pedestrian motion behaviors are analyzed. By calculating the Time to Collision (TTC) and comparing this result with the TTC directly from the ADAS camera, we have shown that our new TTC prediction is more stable and less noisy when contrasted with the older TTC predictions from an ADAS camera system

Driver interaction with vulnerable road users: Modelling driver behaviour in crossing scenarios

Every year, more than 5000 pedestrians and 2000 cyclists die on European roads. These vulnerable road users (VRUs) are especially at risk when interacting with cars. Intelligent safety systems (ISSs), designed to mitigate or avoid crashes between cars and VRUs, first entered the market a few years ago, and still need to be improved to be effective. Understanding how drivers interact with VRUs is crucial to improving the development and the evaluation of ISSs. Today, however, there is a lack of knowledge about driver behaviour in interactions with VRUs. To address this deficiency and contribute to realising the full potential of ISSs, this thesis has multiple objectives: 1) to investigate and describe the driver response process when a VRU crosses the driver path, 2) to devise models that can predict the driver response process, 3) to inform Euro NCAP with new knowledge about driver interactions with crossing VRUs that may guide the development of their test scenarios, and 4) to develop a framework for ISS evaluation through counterfactual simulation and analyse the impact of the chosen driver model on the simulation outcome. The thesis results show that the moment when a VRU becomes visible to the driver has the largest influence on the driver’s braking response process in driver-VRU interactions. Data gathered in driving simulators and on a test track were used to devise different predictive models: one model for the pedestrian crossing scenario, and three for the cyclist crossing scenario. The model for the pedestrian crossing scenario can estimate the moments at which key components of the driver response process (e.g. gas pedal fully released and brake onset) happen. For the cyclist crossing scenario, the first model predicts the brake onset time and the second predicts the experienced discomfort score given the cyclist appearance time. The third predicts the continuous deflection signal of the brake pedal based on the interaction of two visually-derived cues (looming and projected post-encroachment time). These models could be used to improve the design and evaluation of ISSs. From the models, appropriate warning or intervention times that are not a nuisance to the drivers could be adopted by the ISSs, therefore maximizing driver acceptance. Additionally, the models could be used in counterfactual simulations to evaluate ISS safety benefits. In fact, it was shown that driver models are a critical part of these simulations, further demonstrating the need for the development of more realistic driver models. The knowledge provided by this thesis may also guide Euro NCAP towards an improved ISS test protocol by providing information about scenarios that have not yet been evaluated

Risk assessments and modeling of driver by using Risk Potential theory

Recently, various self-driving and driving assistance systems such as Advanced Driver Assistance System (ADAS) have been developed with the intent to reduce the number of motor vehicle accidents. While self-driving systems have been proven to reduce traffic accidents, the systems sometimes make other drivers confused because of their mechanical behavior. To avoid confusion and possible error, it is necessary to construct self-driving systems that exhibit human-like behaviors. Risk Potential theory has been used to construct models that successfully represent driver behavior, especially expert behavior. This project uses Risk Potential theory to construct and evaluate a collision avoidance driver model which uses braking to avoid potential collisions with pedestrians. As a first step, a basic driver model which uses Risk Potential theory is constructed and evaluated using metrics such as collision avoidance, comfortability, and false alarm avoidance. Second, human driving data is collected to observe driver’s risk perception during interactions with a pedestrian. Finally, our proposed driver models improve on standard RP model’s performance but comparisons of the models with observed human performance reveal opportunities for further improvement