基于驾驶员信息处理的道路安全性评价模型

在车辆行驶过程中,行车环境包含了大量的交通信息.道路条件以及管理手段等因素最终影响到驾驶员的信息处理强度.文章通过探讨驾驶员信息处理过程,建立了一种基于驾驶员信息处理强度的道路交通安全评价模型.在示例中,把道路交通信息量定义为道路几何曲率的一次函数,并结合相邻路段运行速度差给出评价指标.     

Using connected vehicle technology to improve the efficiency of intersections

Information from connected vehicles, such as the position and speed of individual vehicles, can be used to optimize traffic operations at an intersection. This paper proposes such an algorithm for two one-way-streets assuming that only a certain percentage of cars are equipped with this technology. The algorithm enumerates different sequences of cars discharging from the intersection to minimize the objective function. Benefits of platooning (multiple cars consecutively discharging from a queue) and signal flexibility (adaptability to demand) are also considered. The goal is to gain insights about the value (in terms of delay savings) of using connected vehicle technology for intersection control.Simulations are conducted for different total demand values and demand ratios to understand the effects of changing the minimum green time at the signal and the penetration rate of connected cars. Using autonomous vehicle control systems, the signal could rapidly change the direction of priority without relying on the reaction of drivers. However, without this technology a minimum green time is necessary. The results of the simulations show that a minimum green time increases the delay only for the low and balanced demand scenarios. Therefore, the value of using cars with autonomous vehicle control can only be seen at intersections with this kind of demand patterns, and could result in up to 7% decrease in delay. On the other hand, using information from connected vehicles to better adapt the traffic signal has proven to be indeed very valuable. Increases in the penetration rate from 0% up to 60% can significantly reduce the average delay (in low demand scenarios a decrease in delay of up to 60% can be observed). That being said, after a penetration rate of 60%, while the delays continue to decrease, the rate of reduction decreases and the marginal value of information from communication technologies diminishes. Overall, it is observed that connected vehicle technology could significantly improve the operation of traffic at signalized intersections, at least under the proposed algorithm.     

Extracting driving characteristics from heavy goods vehicle tachograph charts

European Union regulations require haulage companies of member states like the UK to keep records of their drivers’ hours of work. All heavy goods vehicles (HGV's) over 7.5 tonnes are fitted with tachographs which record a driver's operating activities (periods of driving, other work and rest). These records are etched onto a laminated chart by various styli, one of which records the vehicle's speed. This paper describes the development and testing of a new technique for extracting individual driving characteristics from the speed trace of an HGV tachograph chart to calculate four parameters: distance travelled, average speed, time travelled and speed variability.

The average speed, time travelled and speed variability were analysed statistically using one‐way analysis of variance tests. Speed variability was found to be particularly useful for identifying differences between individual driver's behaviour. Once differences in behaviours can be identified it may be possible to link certain driving habits to factors such as component wear, accident rates and excessive fuel usage.     

Autonomous vehicle control systems for safe crossroads

This article presents a cooperative manoeuvre among three dual mode cars – vehicles equipped with sensors and actuators, and that can be driven either manually or autonomously. One vehicle is driven autonomously and the other two are driven manually. The main objective is to test two decision algorithms for priority conflict resolution at intersections so that a vehicle autonomously driven can take their own decision about crossing an intersection mingling with manually driven cars without the need for infrastructure modifications. To do this, the system needs the position, speeds, and turning intentions of the rest of the cars involved in the manoeuvre. This information is acquired via communications, but other methods are also viable, such as artificial vision. The idea of the experiments was to adjust the speed of the manually driven vehicles to force a situation where all three vehicles arrive at an intersection at the same time.     

Adaptive cruise control design for active congestion avoidance

We present an adaptive cruise control (ACC) strategy where the acceleration characteristics, that is, the driving style automatically adapts to different traffic situations. The three components of the concept are the ACC itself, implemented in the form of a car-following model, an algorithm for the automatic real-time detection of the traffic situation based on local information, and a strategy matrix to adapt the driving characteristics (that is, the parameters of the ACC controller) to the traffic conditions. Optionally, inter-vehicle and infrastructure-to-car communication can be used to improve the accuracy of determining the traffic states. Within a microscopic simulation framework, we have simulated the complete concept on a road section with an on-ramp bottleneck, using empirical loop-detector data for an afternoon rush-hour as input for the upstream boundary. We found that the ACC vehicles improve the traffic stability and the dynamic road capacity. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were already significantly reduced for much lower penetration rates. The efficiency of the proposed driving strategy even for low market penetrations is a promising result for a successful application in future driver assistance systems.     

Eco-driver training within the City of Calgary’s municipal fleet: Monitoring the impact

This article highlights eco-driving as an available policy option to reduce climate altering GHG emissions. Recognizing the need to reduce the environmental impact of its fleet operations, the City of Calgary is a leader in developing programs and policies that aim to reduce GHG emissions and associated pollutants resulting from the use of fossil fuels. Among local action taken against climate change, the City sought to quantify CO₂ emissions reductions from their municipal fleet as a result of eco-driver training, with a specific focus on engine idling. Fifteen drivers from the Development & Building Approvals Business Unit had in-vehicle monitoring technology (CarChips®) installed into their vehicles as part of a three-phase research process. The results show that gasoline and hybrid vehicles decreased average idling between 4% and 10% per vehicle per day, leading to an average emissions decrease of 1.7 kg of CO₂ per vehicle per day.