A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed

A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency

Motorcycle Eyes

As technology continues to advance the safety factor has increased for vehicles on the roads. However, not much has been done to help improve the safety of motorcyclist. To help to solve this problem a wireless blind spot indicator for a motorcycle helmet will be designed. It will be powered off the motorcycle and have indication zones for the left, right, and rear blind spots. The system will alert the rider if a vehicle is within 7 meters of the back of the motorcycle in any of the mentioned blind spots. The radar sensors will detect the vehicle and send a signal to a microcontroller on the motorcycle which will wirelessly communicate to another microcontroller in the helmet via Bluetooth. The microcontroller in the helmet will then indicate to the rider which blind spot in occupied. This technology is already being utilized on cars and now it can hopefully be used to help insure the safety of motorcyclist as well

A phased array antenna system of a millimeter-wave FMCW radar for blind spot detection of mobile robots

Mobile robots have been extensively used in manufacturing plants for inter-logistic transportation in recent years. This paper covers a phased array antenna design for a millimeter wave radar system to improve lidar-based navigation systems' safety and environmental consciousness. The K-band phased array antenna, when integrated with 24 GHz Frequency-Modulated-Continuous-Wave (FMCW) radar, not only enhances the accuracy of the 2-D Area Scanning lidar system but also helps with the safe operation of the vehicle. The safety improvement is made by covering blind spots to mitigate collision risks during the rotations. The paper first reviews the system-level details of the 2D lidar sensor and shows the blind spots when integrated into a Mobile Robot prototype. Then continues with the inclusion of an FMCW Low-Speed Ramp radar system and discusses the design details of the proposed K-band antenna array, which will be integrated with a radar sensor

Semi-autonomous vehicles as a cognitive assistive device for older adults

Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas. Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs. This paper explores if SAVs could be used as a cognitive assistive device for older aging drivers with cognitive challenges. We illustrate the impact of age-related changes of cognitive functions on driving capacity. Furthermore, following an overview on the current state of SAVs, we propose a model for connecting cognitive health needs of older drivers to SAVs. The model demonstrates the connections between cognitive changes experienced by aging drivers, their impact on actual driving, car sensors' features, and vehicle automation. Finally, we present challenges that should be considered when using the constantly changing smart vehicle technology, adapting it to aging drivers and vice versa. This paper sheds light on age-related cognitive characteristics that should be considered when developing future SAVs manufacturing policies which may potentially help decrease the impact of cognitive change on older adult drivers