Assessment of traffic impact on future cooperative driving systems: challenges and considerations

Connect & Drive is a start-up project to develop a cooperative driving system and improve the traffic performance on Dutch highways. It consists of two interactive subsystems: cooperative adaptive cruise control (CACC) and connected cruise control (CCC). To assess the traffic performance, a traffic simulation model will be established for large-scale evaluation and providing feedbacks to system designs. This paper studies the factors determining the traffic performance and discusses challenges and difficulties to establish such a traffic simulation model

Driver behaviour with adaptive cruise control

This paper reports on the evaluation of adaptive cruise control (ACC) from a psychological perspective. It was anticipated that ACC would have an effect upon the psychology of driving, i.e. make the driver feel like they have less control, reduce the level of trust in the vehicle, make drivers less situationally aware, but workload might be reduced and driving might be less stressful. Drivers were asked to drive in a driving simulator under manual and ACC conditions. Analysis of variance techniques were used to determine the effects of workload (i.e. amount of traffic) and feedback (i.e. degree of information from the ACC system) on the psychological variables measured (i.e. locus of control, trust, workload, stress, mental models and situation awareness). The results showed that: locus of control and trust were unaffected by ACC, whereas situation awareness, workload and stress were reduced by ACC. Ways of improving situation awareness could include cues to help the driver predict vehicle trajectory and identify conflicts

Framework for Data Acquisition and Fusion of Camera and Radar for Autonomous Vehicle Systems

The primary contribution is the development of the data collection testing methodology for autonomous driving systems of a hybrid electric passenger vehicle. As automotive manufacturers begin to develop adaptive cruise control technology in vehicles, progress is being made toward the development of fully-autonomous vehicles. Adaptive cruise control capability is classified into five levels defined by the Society of Automotive Engineering. Some vehicles under development have attained higher levels of autonomy, but the focus of most commercial development is Level 2 autonomy. As the level of autonomy increases, the sensor technology becomes more advanced with a sensor suite which includes radar, camera, and vehicle-to-everything radio. Sensors must detect the objects around the vehicle to be able for communicate the data to the adaptive cruise control algorithm. If a vehicle is in an accident, the driver is typically responsible for the damages, but with an autonomous vehicle, there might not be a driver. A process to guarantee a vehicle will perform as it was developed is critical to a vehicle’s development and testing. The goal of this work is to implement a verification and validation system that can be used on adaptive cruise control systems. The system developed in this paper used different testing environments such as model-in-the-loop, hardware-in-the-loop, and vehicle-in-the-loop, to fully validate an autonomous vehicle. A systematic data acquisition process has been developed to support autonomous vehicle development. The data that was taken had an organized way of comparing the results in each environment. Requirements management, vehicle logbook, and test case creation played a vital role in combining the information across the environments. The method produced a consumer-ready adaptive cruise control system in a 2019 Chevrolet Blazer RS. The vehicle was able to perform at an Advanced Vehicle Technology Competition where the adaptive cruise control system placed 1st in Connected and Automated Vehicle Perception System & Adaptive Cruise Control Drive Quality Evaluation. Results are presented that illustrate the utility of the data acquisition and multi-layer testing process for autonomous vehicle development

Predictive Braking With Brake Light Detection-Field Test

Driver assistance systems, such as adaptive cruise control, are an increasing commodity in modern vehicles. Our earlier experience of radar-based adaptive cruise control has indicated repeatable abrupt behavior when approaching a stopped vehicle at high speed, which is typical for extra-urban roads. Abrupt behavior in assisted driving not only decreases the passenger trust but also reduces the comfort levels of such systems. We present a design and proof-of-concept of a machine vision-enhanced adaptive cruise controller. A machine vision-based brake light detection system was implemented and tested in order to smoothen the transition from coasting to braking and ensure speed reduction early enough. The machine vision system detects the brake lights in front, then transmits a command to the cruise controller to reduce speed. The current paper reports the speed control system design and experiments carried out to validate the system. The experiments showed the system works as designed by reducing abrupt behavior. Measurements show that brake light-assisted cruise control was able to start deceleration about three seconds earlier than a cruise controller without brake light detection. Measurements also showed increased ride comfort with the maximum deceleration and minimum jerk levels improving from 5% to 31%.Peer reviewe

Evolution of the cruise control

In this paper we discussed the evolution of Cruise Control systems, from the most rudimentary systems to the adaptive systems. Different Cruise Control systems are presently implemented in vehicles on the market today and the prospects for development of them.info:eu-repo/semantics/publishedVersio

Automated Merging in a Cooperative Adaptive Cruise Control (CACC) System

Cooperative Adaptive Cruise Control (CACC) is a form of cruise control in which a vehicle maintains a constant headway to its preceding vehicle using radar and vehicle-to-vehicle (V2V) communication. Within the Connect & Drive1 project we have implemented and tested a prototype of such a system, with IEEE 802.11p as the enabling communication technology. In this paper we present an extension of our CACC system that allows vehicles to merge inside a platoon of vehicles at a junction, i.e., at a pre-defined location. Initially the merging vehicle and the platoon are outside each other’s communication range and are unaware of each other. Our merging algorithm is fully distributed and uses asynchronous multi-hop communication. Practical testing of our algorithm is planned for May 2011

Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)

Cooperative Adaptive Cruise Control (CACC) is one of the driving applications of vehicular ad-hoc networks (VANETs) and promises to bring more efficient and faster transportation through cooperative behavior between vehicles. In CACC, vehicles exchange information, which is relied on to partially automate driving; however, this reliance on cooperation requires resilience against attacks and other forms of misbehavior. In this paper, we propose a rigorous attacker model and an evaluation framework for this resilience by quantifying the attack impact, providing the necessary tools to compare controller resilience and attack effectiveness simultaneously. Although there are significant differences between the resilience of the three analyzed controllers, we show that each can be attacked effectively and easily through either jamming or data injection. Our results suggest a combination of misbehavior detection and resilient control algorithms with graceful degradation are necessary ingredients for secure and safe platoons.Comment: 8 pages (author version), 5 Figures, Accepted at 2017 IEEE Vehicular Networking Conference (VNC